Minutia features generation apparatus, system, minutia features generation method, and program

ABSTRACT

A minutia features generation apparatus comprises: an input part to input an image formed as a curved stripe pattern by a ridge line(s); a generation part to generate a skeleton image formed by extracting a skeleton(s) from the image; an extraction part to extract a plurality of minutiae from the skeleton image; and a calculation part configured to calculate a relation minutia feature(s) representing relationship between a first minutia and a second minutia among the plurality of minutiae, wherein the calculation part calculates as one of the relation minutia features defined by a crossing count of the skeleton(s) and a straight line connecting from the second minutia to a nearest neighbor point which is a point on a trace line traced by tracing starting from the first minutia, which point is located at a shortest line distance from the second minutia.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/628,537 filed Jan. 3, 2020 which is a National Stage of InternationalApplication No. PCT/JP2018/025546 filed Jul. 5, 2018, based upon andclaims the benefit of the priority of Japanese Patent Application No.2017-132441 (filed on Jul. 6, 2017), the disclosure of which isincorporated herein in its entirety by reference. The present inventionrelates to a minutia features generation apparatus, system, minutiafeatures generation method and program. More specifically, the presentinvention relates to a minutia features generation apparatus, a system,a minutia features generation method and a program for processing imagedata of curved striped pattern, specifically, fingerprint image or thelike.

FIELD Background

A fingerprint and a palmprint configured of a lot of ridge lines in acurved striped pattern are used as means for personal identificationsince a long time. Especially, a matching processing using a latentfingerprint left at crime scenes is an effective crime investigationmeans. Many of police agencies have introduced a computer-basedfingerprint matching system (AFIS; Automated Fingerprint IdentificationSystem).

In the fingerprint matching system, identification of a personcorresponding to the latent fingerprint is made by matching each featurepoints (hereinafter referred as minutia(e)) of the latent fingerprintcollected at crime scenes with the fingerprint minutiae registered in adata base. For the minutiae (fingerprint minutia) used by thefingerprint matching, end points or bifurcation points of thefingerprint ridge lines are often used. For example, “4.3 Minutia-BasedMethod” in Non Patent Literature 1 discloses methods for minutiamatching using the end points and the bifurcation points of thefingerprint ridge lines.

When matching high-quality fingerprint images such as an impressedfingerprint(s), the quality of the fingerprint image is high enough, sothat presence of significant image distortion is rare. Therefore, incase of between high-quality fingerprint images, locations wherecorresponding minutiae (so-called paired minutiae) exist are also at thesimilar positions. Also, as for the fingerprint image such as theimpressed fingerprint, there are a sufficient number of minutiae in manycases. For these reasons, in the matching process of high-qualityfingerprint image such as the impressed fingerprints, it is oftenpossible to ensure high matching accuracy without executing specialprocessing. That is, high matching accuracy can be guaranteed even bythe existing minutia matching technology.

However, as described above, when fingerprint matching is regarded as ameans for the crime investigation, it is often necessary to perform thematching process between the impressed fingerprint and the latentfingerprint. Images related to the latent fingerprint often has anoticeable image distortion or has a small area image showing only apart of the entire fingerprint. In such matching process of the latentfingerprints and the impressed fingerprint, the locations of thecorresponding minutia may be significantly different between theimpressed fingerprint and the latent fingerprint due to the imagedistortion of the latent fingerprint. Still more, in case the latentfingerprint is an image of a small area with a small size, it may not bepossible to extract a sufficient number of minutiae necessary for thematching process from the latent fingerprint concerned.

Under these circumstances, it is difficult to ensure high accuracy inthe matching process of latent fingerprints and impressed fingerprints.

In view of the above, various techniques have been proposed forimproving the accuracy of matching between the latent fingerprints andthe impressed fingerprints. For example, Non Patent Literature 2 and 3disclose that not only the minutia but also “inter-minutia ridge linecount” is used as a new minutia feature.

In the Non Patent Literature (NPL) 2 and 3, the “inter-minutia ridgeline count” is calculated as a new minutia feature. For the calculationof “inter-minutia ridge line count” in Non Patent Literatures 2 and 3,firstly, the skeletons (ridge lines) are extracted from the fingerprintimage. After that, the minutiae are extracted from the extractedskeletons, and a network between each of the minutiae is calculatedbased on a relationship between each minutia and a minutia located inthe vicinity thereof. After that, the “inter-minutia ridge line count”is calculated as the minutia features in addition to the location(s) andthe direction(s) of the minutia(e), and the type of minutia such as theend point or the bifurcation point, and the connection relationship ofthe minutia network(s). The “inter-minutia ridge line count” iscalculated as the number of intersections between edges (line segmentsconnecting minutiae one to another) and the ridge lines in the minutiapoint network. That is, the “inter-minutia ridge line count” is thenumber of ridge lines intersecting with a straight line when twominutiae are connected by the straight line concerned. More precisely,the number of intersections between the straight line connecting twominutiae and the ridge lines that are not directly connected with thetwo minutiae concerned represents the “inter-minutia ridge line count”.

Patent Literature (PTL) 1 discloses that “ridge connection relationinformation” between two neighboring minutiae is used as a new minutiafeature. Patent Literatures 2 to 4 disclose extraction of the minutiae,and calculation of various minutia features featuring the minutia, andthe like.

Patent Literature 1 discloses a technique of tracing skeleton(s) toextract “ridge connection relation information” from a target minutia toa neighboring minutia. Further, in Patent Literature 1 discloses amethod for a skeleton tracing, in which, as a starting point of theskeleton tracing, not only the target minutia and the neighboringminutia, but also a projected minutia is defined in an orthogonaldirection from each minutia, and the skeleton tracing is performed fromthe defined projected minutia. The number of ridge lines included in the“ridge connection relation information” disclosed in Patent Literature 1is more robust against image distortion, unlike the “inter-minutia ridgeline count” disclosed in Non Patent Literatures 2 and 3.

However, the technique disclosed in Patent Literature 1 has a problem inthat the number of ridge lines between any of two minutia points cannotbe extracted because the first minutia encountered by the skeletontracing is extracted as the neighboring minutia. Also, since by PatentLiterature 1, the minutiae are extracted using the skeleton tracing, incase the skeleton is interrupted due to a noise (for example, blurringof the image) or presence of a sweat pore(s), a minutia existing beyondthe interruption cannot be detected as the neighboring minutia. Here,the problem will be described with reference to FIG. 37. Note that theFIG. 37 is a diagram that is drafted with additional illustration toexplain above mentioned problem from FIG. 9 in Patent Literature 1.

Referring to FIG. 37, the skeleton connected to a target minutia P54 istraced, and the minutia P55 is extracted. Since the minutia P55 isconnected to the same skeleton as the target minutia P54, the order (n)becomes “0”. A distance between the target minutia P54 and the minutiapoint P55 is regarded as a minutia feature (0th order principaldistance). As described in Patent Literature 1, the minutia feature (0thorder principal distance) represents the distance from a nearestconnected neighboring minutia, however, there is a wide variety ofconnection types between minutiae, so that such minutia feature has alimited contribution to the accuracy improvement in the matchingprocess.

Moreover, in Patent Literature 1, a vertical line is drawn in a verticaldirection from the target minutia P54, and a point of intersection ofthe vertical line with a skeleton is calculated as a “trace distancemeasurement starting point”. In Patent Literature 1, by the skeletontracing performed from this trace distance measurement starting point,the first minutia encountered is extracted as the neighboring minutia,and the distance between the trace distance measurement starting pointand the neighboring minutia is calculated as another minutia feature.For example, the distance between the trace distance measurementstarting point Q54 and the minutia P53 is calculated as a “first ordersub-distance”.

The analyses on the literatures of the citation list in the disclosureof the present application have been made by the inventors of thepresent invention.

-   PTL 1: Japanese Patent Kokai Publication No. H11-195119A-   PTL 2: Japanese Patent Kokoku Publication No. S60-012674B-   PTL 3: WO. Patent Publication No. 2017-038695A-   PTL 4: Japanese Patent Kokai Publication No. 2003-274006A-   NPL 1: D. Maltoni, “Handbook of Fingerprint Recognition”, Springer,    2003-   NPL 2: K. Asai et al., “Automated Fingerprint Identification by    Minutia-Network Feature—Feature Extraction Process—”, Journal of the    Institution of Electronics, Information and Communication Engineer,    D-II Vol. J72-D-II, No. 5, pp. 724-732, May 1989.-   NPL 3: K. Asai et al., “Automated Fingerprint Identification by    Minutia-Network Feature—Matching Processes—”, Journal of the    Institution of Electronics, Information and Communication Engineer,    D-II Vol. J72-D-II, No. 5, pp. 733-740, May 1989.

SUMMARY

As described above, Patent Literature 1 proposes the “inter-minutiaridge lines count” that is robust against the image distortion. However,in the fingerprint image, there may exist a skeleton that is notconnected to the target minutia and does not intersect with the straightline extending in the vertical direction from the target minutia as inthe skeletons as shown in FIG. 37. In this case, an end point is formedas a minutia 402 on a skeleton 401, however, the minutia 402 cannot beextracted as the neighboring minutia by the method disclosed in PatentLiterature 1.

It is a main object of the present invention to provide a minutiafeatures generation apparatus, a system, a minutia features generationmethod, and a program that calculate minutia features which contributeto the improvement of matching accuracy.

According to a first aspect of the invention, there is provided aminutia features generation apparatus, comprising: an input partconfigured to input an image formed as a curved stripe pattern by aridge line(s); a generation part configured to generate a skeleton imageformed by extracting a skeleton(s) from the image; an extraction partconfigured to extract a plurality of minutiae from the skeleton image;and a calculation part configured to calculate a relation minutiafeature(s) representing relationship between a first minutia and asecond minutia among the plurality of minutiae, wherein, the calculationpart calculates as one of the relation minutia features defined by acrossing count of the skeleton(s) and a straight line connecting fromthe second minutia to a nearest neighbor point which is a point on atrace line traced by tracing starting from the first minutia, whichpoint is located at a shortest line distance from the second minutia.

According to a second aspect of the invention, there is provided asystem comprising: a minutia features generation apparatus, and amatching apparatus configured to perform matching of images using theminutia features generated by the minutia features generation apparatus,wherein the minutia features generation apparatus comprises: an inputpart configured to input an image formed as a curved stripe pattern by aridge line(s); a generation part configured to generate a skeleton imageformed by extracting a skeleton(s) from the image; an extraction partconfigured to extract a plurality of minutiae from the skeleton image;and a calculation part configured to calculate a relation minutiafeature(s) representing relationship between a first minutia and asecond minutia among the plurality of minutiae, wherein the calculationpart calculates as one of the relation minutia features defined by acrossing count of the skeleton(s) and a straight line connecting fromthe second minutia to a nearest neighbor point which is a point on atrace line traced by tracing starting from the first minutia, whichpoint is located at a shortest line distance from the second minutia.

According to a third aspect of the invention, there is provided aminutia features generation method comprising, inputting an image formedas a curved stripe pattern by a ridge line(s); generating a skeletonimage formed by extracting a skeleton(s) from the image; extracting aplurality of minutiae from the skeleton image; and calculating arelation minutia feature(s) representing relationship between a firstminutia and a second minutia among the plurality of minutiae, whereinthe step of calculation to calculate as one of the relation minutiafeatures defined by a crossing count of the skeleton(s) and a straightline connecting from the second minutia to a nearest neighbor pointwhich is a point on a trace line traced by tracing starting from thefirst minutia, which point is located at a shortest line distance fromthe second minutia.

According to a fourth aspect of the invention, there is provided aprogram configured to cause a computer to execute: a process ofinputting to input an image formed as a curved stripe pattern by a ridgeline(s); a process of generating a skeleton image formed by extracting askeleton(s) from the image; a process of extraction to extract aplurality of minutiae from the skeleton image; and a process ofcalculation to calculate a relation minutia feature(s) representingrelationship between a first minutia and a second minutia among theplurality of minutiae, wherein the process of calculation to calculateas one of the relation minutia features defined by a crossing count ofthe skeleton(s) and a straight line connecting from the second minutiato a nearest neighbor point which is a point on a trace line traced bytracing starting from the first minutia, which point is located at ashortest line distance from the second minutia. The program may berecorded on a computer readable storage medium. The storage medium maybe non-transient such as semiconductor memory, a hard disk, a magneticrecording medium, an optical recording medium or the like. The presentinvention may be embodied as a computer program product.

According to each aspect of the present invention, there are providedthe minutia features generation apparatus, the system, the minutiafeatures generation method, and the program that contribute to calculatethe minutia features contributing to the improvement of matchingaccuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram describing an outline of an exemplary embodiment.

FIG. 2 is a diagram showing an example of a configuration of afingerprint matching system according to a first exemplary embodiment.

FIG. 3 is a diagram showing an example of the fingerprint imageaccording to the first exemplary embodiment.

FIG. 4 is a block diagram showing an example of hardware configurationfor a minutia features generation apparatus according to the firstexemplary embodiment.

FIG. 5 is a diagram showing an example of process configuration for theminutia features generation apparatus according to the first exemplaryembodiment.

FIG. 6 is a diagram showing an example of a skeleton image.

FIG. 7 is a diagram showing an example of a ridge line direction data.

FIG. 8 shows a diagram showing an example of minutiae.

FIG. 9 is a diagram showing an example of process configuration for aminutia features calculation part according to the first exemplaryembodiment.

FIG. 10 shows a diagram explaining a calculation of a minutia direction.

FIG. 11 shows a diagram explaining a calculation of the minutiadirection.

FIG. 12 shows a diagram showing an example of basic minutia features.

FIG. 13 is a flowchart showing an example of operations of a calculationpart of relation minutia features according to the first exemplaryembodiment.

FIG. 14 shows a diagram explaining tracing of a ridge line using ridgeline direction data.

FIG. 15 shows a diagram explaining tracing of the ridge line using theridge line direction data.

FIG. 16 shows a diagram explaining calculation of a child minutiarelative position.

FIG. 17 shows a diagram explaining calculation of a relation minutiafeature(s).

FIG. 18 shows a diagram showing an example of the relation minutiafeatures.

FIG. 19 is a diagram showing an operation of a calculation part ofconnection type according to the first exemplary embodiment.

FIG. 20 is a flowchart showing an example of operations of thecalculation part to calculate a connection type according to the firstexemplary embodiment.

FIG. 21 is a flowchart showing an example of operations of thecalculation part to calculate the connection type according to the firstexemplary embodiment.

FIG. 22 shows a diagram explaining connection types.

FIG. 23 shows a diagram explaining connection types.

FIG. 24 shows a diagram explaining connection types.

FIG. 25 shows a diagram explaining connection types.

FIG. 26 shows a diagram explaining connection types.

FIG. 27 is a flowchart showing an example of other operation of acalculation part to calculate the connection type according to the firstexemplary embodiment.

FIG. 28 is a flowchart showing an example of other operations of thecalculation part to calculate the connection type according to the firstexemplary embodiment.

FIG. 29 shows a diagram showing an example of the minutia features whichare an output of a minutia features output part.

FIG. 30 is a diagram showing an example of process configuration for amatching apparatus according to the first exemplary embodiment.

FIG. 31 shows a diagram showing an example of the minutia featurescalculated by the matching apparatus according to the first exemplaryembodiment.

FIG. 32 is a flowchart showing an example of operations of a matchingpart according to the first exemplary embodiment.

FIG. 33 shows a diagram showing an example of paired minutiae.

FIG. 34 is a diagram showing an example of matching result by a matchingapparatus according to the first exemplary embodiment.

FIG. 35 is a sequence diagram showing an example of operations of thefingerprint matching system according to the first exemplary embodiment.

FIG. 36 shows a diagram explaining a technical problem(s) disclosed inPatent Literatures 2 and 3.

FIG. 37 shows a diagram explaining a technical problem(s) of thetechnology disclosed in Patent Literature 1.

MODES

First, an overview of an exemplary embodiment will be described.Reference signs in each drawing given in this overview are given to eachelement for convenience as an example to help understanding, and thedescription of this overview does not intend to impose any limitation.

Connection lines between blocks in respective diagrams may be bothbidirectional and unidirectional. Unidirectional arrows schematicallyshow flow of main signals (data), but do not exclude bidirectionality.In addition, although not explicitly disclosed, in the circuit diagrams,block diagrams, internal configuration diagrams, connection diagrams andthe like, shown in the disclosure of the present disclosure, input portsand output ports are present at respective input terminals and outputterminals of each connection line. The same applies for input/outputinterfaces.

A minutia features generation apparatus 100 according to one exemplaryembodiment includes an input part 101, a generation part 102, anextraction part 103, and a calculation part 104. The input part 101inputs an image in which a curved stripe pattern is formed by ridgelines. The generation part 102 generates a skeleton image obtained byextracting a thin line (termed “skeleton” herein) from the image. Theextraction part 103 extracts a plurality of minutiae from the skeletonimage. The calculation part 104 calculates a relation minutia feature(s)that indicates a relationship between the first minutia and the secondminutia among the plurality of minutiae. Further, the calculation partcalculates, as one of relation minutia features, a count of crossing ofthe skeletons and a straight line connecting from the second minutia toa nearest neighbor point which is a point on a trace line obtained bytracing starting from the first minutia, which point is located at ashortest line distance from the second minutia point.

The minutia features generation apparatus 100 arbitrarily selects twominutiae among the minutiae extracted from the skeleton image, and setsthem as a target of calculation for relation minutia features. Forexample, the minutia features generating apparatus 100 extracts apredetermined number of minutiae in the order of the close distance tothe minutia (first minutia, or parent minutia to be described later),and selects as a target minutia (second minutia, or child minutia to bedescribed later) for calculating the relation minutia features. For thisreason, even minutiae that could not be made target in Patent Literature1 can be used as a target for the minutia features calculation.

Also, the minutia features generation apparatus 100 calculates a numberof skeletons (ridge lines) existing between the first minutia and thesecond minutia as one of the relation minutia features. At that time,the minutia features generating apparatus 100 calculates the relationminutia features by using the minutia that is closest to the secondminutia (the nearest neighbor minutia) which is on the trace lineobtained by the tracing starting from the first minutia. That is, theminutia features generating apparatus 100 calculates the trace line bythe tracing (such as a ridge line direction tracing, to be describedlater) starting from the first minutia, and calculates a count ofskeleton(s) between the nearest neighbor minutia which is on the traceline and whose distance from the second minutia is the closest and thesecond minutia, so that it is possible to calculate a minutia featurethat is not affected by image distortion.

In addition, since the relation minutia feature calculated by theminutia features generating apparatus 100 increases the minutia featurethat features the minutia extracted from the image, the matchingaccuracy can be improved even in the case where an image extracted onlyby a small number of minutiae is the target of the matching.

As described above, the minutia features generation apparatus 100according to the exemplary embodiment can generate a minutia featurethat can realize high matching accuracy even in the case where an imagewith remarkable distortion or an image of a small region is the matchingtarget.

Hereinafter, the exemplary embodiment will be concretely described infurther detail, with reference to the figures. The same referencenumeral is given to the same component(s) in the respective exemplaryembodiments, and description thereof will be omitted.

First Exemplary Embodiment

A first exemplary embodiment will be described in detail with referenceto the drawings.

FIG. 2 is a diagram showing an example of the configuration of thefingerprint matching system according to the first exemplary embodiment.By referring to FIG. 2, the fingerprint matching system is configured bycomprising, a minutia features generation apparatus 10, a database 20,and a matching apparatus 30.

The minutia features generation apparatus 10 is an apparatus thatreceives as input data related to a fingerprint image and its ID(Identifier) information, and generates minutia features featuring theinput fingerprint image. The minutia features generation apparatus 10outputs the input fingerprint image, minutia features calculated fromthe fingerprint image, and ID information of the fingerprint image tothe database 20.

The database 20 is an apparatus that stores the fingerprint image, itsminutia features, and ID information associated with each other.

The matching apparatus 30 is an apparatus that receives the fingerprintimage and performs matching process on the fingerprint image. Thematching device 30 performs matching process on the fingerprint image,by calculating the minutia features of the same type as the minutiafeatures generated by the minutia features generation apparatus 10 fromthe input fingerprint image, and by using the calculated minutiafeatures and the minutia features stored in the database 20. Thematching apparatus 30 outputs a candidate of fingerprint image havingthe same or similar minutia features, by comparing the minutia featuresobtained from the input fingerprint image with the minutia featuresobtained from the database 20.

Here, impressed fingerprints are fingerprints collected for the purposeof registration in the database or the like, and has a characteristicthat the area of the ridge line is broad, and the quality is high. Onthe other hand, latent (residual) fingerprints are those left behind atcrime scenes or the like, in which there are frequently cases whereimage distortion is significant, and the area of clear ridge lineregion(s) is small.

In general, it is often difficult to perform matching using the latentfingerprint with low quality. In the viewpoint of above, in the firstexemplary embodiment, the minutia features generation apparatus 10receives as input an image related to the impressed fingerprint, thematching apparatus 30 receives a fingerprint image related to the latentfingerprint, and the following explanation will be given.

FIG. 3 is a diagram showing an example of the fingerprint imageaccording to the first exemplary embodiment. Here, the examples of thefingerprint image starting from FIG. 3 are images obtained by digitizingthe fingerprint image read by a sensor or a scanner.

The fingerprint images shown in FIG. 3(a) and FIG. 3(b) are an exampleof fingerprint images of the same finger of the same person. Comparingthe two fingerprint images of FIG. 3(a) and FIG. 3(b), it can be seenthat the fingerprint image shown in FIG. 3(b) has remarkable imagedistortion compressed in the vertical direction.

FIG. 3(c) shows an example of the fingerprint image obtained by cuttingout a part of the fingerprint image shown in FIG. 3(b). The fingerprintimage shown in FIG. 3(c) is a partial area of the fingerprint imageshown in FIG. 3(b), and the fingerprint image can be treated (orregarded) as a latent fingerprint. The fingerprint image shown in FIG.3(c) is not easy for matching because the image has remarkabledistortion and the ridge line area is narrow.

In the first exemplary embodiment, explanation follows provided that thefingerprint image shown in FIG. 3(a) is the impressed fingerprint (thefingerprint image whose minutia features are registered in the database20), and the fingerprint image shown in FIG. 3 (c) is the target ofmatching as the latent fingerprint. That is to say, the minutia featuresgeneration apparatus 10 calculates the minutia features from thefingerprint image (impressed fingerprint) shown in FIG. 3(a), and thematching apparatus 30 calculates the minutia features from thefingerprint image (latent fingerprint) shown in FIG. 3 (c).

However, to be described later on, since the minutia featurescalculation process of the matching apparatus 30 can be the same as theminutia features calculation process of the minutia features generationapparatus 10, in the following description, images related to theimpressed fingerprint and the latent fingerprint will be describedtogether. Also, in the first exemplary embodiment, a case will bedescribed in which the minutia features generation apparatus 10 handlesthe impressed fingerprint and the matching apparatus 30 handles thelatent image fingerprint, however, it is not to be understood that suchdescription will limit the scope of fingerprint image in each apparatusshown in FIG. 2. For example, the matching apparatus 30 may not have theminutia features calculation function, and the minutia featuresgeneration apparatus 10 may generate the minutia features from thelatent fingerprint and provide the minutia features to the matchingapparatus 30. Also, fingerprints to be matched may be between theimpressed fingerprints, or may be between the latent fingerprints.Concretely, for the purpose of criminal investigation, the firstexemplary embodiment is configured as described above in considerationof the fact that there are many cases where matching of the latentfingerprint and the impressed fingerprints is performed.

[Hardware Configuration]

A hardware configuration of various apparatuses making up thefingerprint matching system according to the first exemplary embodimentwill be described.

FIG. 4 is a block diagram showing an example of hardware configurationof the fingerprint minutia features generation apparatus 10 according tothe first exemplary embodiment. The minutia features generationapparatus 10 can be configured by a so-called computer (informationprocessing apparatus), and has a configuration shown in FIG. 4.

For example, the minutia features generation device 10 comprises a CPU(Central Processing Unit) 11, a memory 12, an input output interface 13,a NIC (Network Interface Card) 14 that is a communication interface, andthe like, which are connected to each other via an internal bus.

However, it is not to be understood that the hardware configuration ofminutia features generation apparatus 10 as shown in FIG. 4 is limitedhereto. The minutia features generation apparatus 10 may includehardware which are not shown, or may not include the input outputinterface 13 as necessary. For example, in the case where information isinput or output, to or from the minutia features generation apparatus byan operation terminal connected via a network, the input outputinterface 13 may be unnecessary. Also, it is not to be understood thatthe number of CPU and the like included in the minutia featuresgeneration apparatus 10 is limited to the example shown in FIG. 4, andmultiple CPUs may be included in the minutia features generationapparatus 10.

The memory 12 is a RAM (Random Access Memory), a ROM (Read Only Memory),or an auxiliary storage device (such as a hard disk).

The input output interface 13 is an interface of a display device or aninput device (not shown). The display device is, for example, a liquidcrystal display, and the like. The input device is, for example, adevice that accepts user operations such as a keyboard and a mouse, anda device that inputs (receives) information from an external storagedevice such as a USB (Universal Serial Bus) memory, and the like. Also,included are devices such as a sensor and a scanner that convert thefingerprint images into digital data as input devices. The user inputsnecessary information to the minutia features generation apparatus 10using the keyboard, the mouse, and the like.

The functions of the minutia features generation apparatus 10 arerealized by a processing module(s) described later. The processingmodule, for example, is realized by the CPU 11 executing a programstored in the memory 12. Also, the program can be downloaded via thenetwork or updated using the storage medium that stores the program.Moreover, the processing module may be realized by a semiconductor chip.That is, the functions performed by the processing module only needs tobe realized by at least any one of any hardware and software.

Also, by installing the above-described computer program in the storageunit of the computer, the computer can function as the minutia featuresgeneration apparatus 10. Moreover, by causing the computer program toexecute the above-described computer program, the minutia featuresgeneration method can be executed by the computer.

Also, since the hardware configuration of the minutia featuresgeneration apparatus 10 and the matching apparatus 30 can be the same,explanation on the hardware of the matching apparatus 30 is omitted.Further, since the hardware configuration and operation of the database20 are apparent to those skilled in the art, the explanation thereof isomitted.

[Minutia Features Generation Apparatus]

FIG. 5 is a diagram showing an example of the process configuration ofthe minutia features generation apparatus 10. By referring to FIG. 5,the minutia features generation apparatus 10 is configured bycomprising, a fingerprint image input part 201, a skeleton imagegeneration part 202, a minutia extraction part 203, a minutia featurescalculation part 204, and a minutia features output part.

Also, each part such as the fingerprint image input part 201 isconfigured to be able to exchange data with each other and to accessdata stored in the memory 12. Each part such as the skeleton imagegeneration part 202 uses the memory 12 as a work area and as a storagearea of data.

The fingerprint image input part 201 is a means configured to receivethe fingerprint image (the image in which a curved stripe pattern isformed by ridge lines) and ID information of the fingerprint image. Forexample, the fingerprint image input part 201 imports digitalized data(image file) of the fingerprint image to store in the external storagemedium such as the USB memory, and stores the data in the memory 12. Or,the fingerprint image input part 201 may input (receive) data relatingto the fingerprint image, and the like via the network. Or, not using aconfiguration to input digitized fingerprint image from outside, thefingerprint image input part 201 may digitize the fingerprint imageusing the scanner or the like connected to or incorporated in theminutia features generation apparatus 10.

Here, there is a standardized standard for the fingerprint image.Concretely, the U.S. National Institute of Standards and Technologystandardized the standard described as ANSI/NIST-ITL-1-2000 Data Formatfor the Interchange of Fingerprint, Facial, & Scar Mark & Tattoo (SMT)Information. Now, the standard document can be downloaded from below URLat the timing of the application.https://www.nist.gov/system/files/documents/itl/ansi/sp500-245-a16.pdf

It is preferred that the fingerprint image input part 201 can handle thedigitized fingerprint image (for example, a fingerprint image with aresolution of 500 dpi) based on the above standard.

The skeleton image generation part 202 is a means configured to generatethe skeleton image in which thin lines are extracted from the acquiredfingerprint image. For example, the skeleton generation part 202generates skeleton images as shown in FIG. 6. FIG. 6(a) is a diagramshowing an example of skeleton image that is generated from thefingerprint image (impressed fingerprint) as shown in FIG. 3 (a). FIG.6(b) is a diagram showing an example of skeleton image that is generatedfrom the fingerprint image (latent fingerprint) as shown in FIG. 3 (c).Here, in FIG. 6, for ease of understanding, the skeleton image issuperposed on the fingerprint image.

The skeleton image generation part 202 can use the skeleton imageextraction method disclosed in “3 Fingerprint Analysis andRepresentation” of Non-Patent Literature 1. Therefore, although adetailed description regarding the generation of skeleton image isomitted, the skeleton image generation part 202 generates the skeletonimage in the following outline procedure.

First, the skeleton image generation part 202 extracts the direction ofthe ridge line of the fingerprint image. Thereafter, the skeleton imagegeneration unit 202 enhances contrast of each ridge line along the ridgeline direction, and generates a binary image. Thereafter, the skeletonimage generation part 202 extracts the skeleton data (generates skeletonimage) by thinning the binary image.

Here, the skeleton image generation part 202 generates “ridge linedirection data” and stores it in the memory 12 in the course of theskeleton image generation process. More concretely, the skeleton imagegeneration part 202 calculates the ridge line direction at each point onthe ridge line of the fingerprint image, and generates a set of ridgeline directions as “ridge line direction data”. For example, theskeleton image generation part 202 calculates the ridge line directionof the fingerprint image by setting the point (pixel on the skeleton) atwhich ridge line direction data is calculated as the origin of the XYcoordinate system, and the fingertip of the fingerprint image as the Yaxis positive direction, and by calculating the angle spanned by theridge line direction and X axis. Thereafter, the skeleton imagegeneration part 202 converts (approximates) the calculated angle into adirection obtained by dividing the quadrants of the XY coordinate systemby a predetermined number (such as 16), and generates the ridge linedirection at each point. Here, in case a region where no ridge lineexists is present in the fingerprint image, the skeleton imagegeneration part 202 generates ridge line direction data using ridgedirection data adjacent to the region. For example, it is possible tocope with using the adjacent ridge line direction data as it is, orusing the average of the adjacent ridge line direction data as the ridgeline direction data, or so on.

The set of the ridge line direction at each point including the ridgeline of the fingerprint image becomes the “ridge line direction data”.That is, in addition to the generation of the skeleton image, theskeleton image generation part 202 generates the ridge line directiondata including information on the ridge line direction at each point onthe ridge line forming the fingerprint image. Note that the ridge linedirection data and its utilization are described in the referenceliterature (Japanese Patent Kokai 2007-226746A), so that the skeletonimage generation part 202 can make use of the method disclosed in thisliterature.

FIG. 7 is a diagram showing an example of the ridge line direction data.FIG. 7(a) shows an example of the ridge line direction datacorresponding to the skeleton image shown in FIG. 6(a), and FIG. 7(b)shows an example of the ridge line direction data corresponding to theskeleton image shown in FIG. 6(b). Note that, in FIG. 7, the ridge linedirection data is displayed superposed on the fingerprint image as inFIG. 6.

The minutia extraction part 203 is a means for extracting minutiae fromthe skeleton image. More concretely, the minutia extraction part 203extracts minutiae by extracting bifurcation points and end points of theskeleton lines from the skeleton image generated by the skeleton imagegeneration part 202. Furthermore, the minutia extraction methoddisclosed in “3 Fingerprint Analysis and Representation” of Non-PatentLiterature 1 can be used as a procedure for extracting minutia from theskeleton image. Therefore, the detailed explanation regarding on minutiaextraction is omitted. In addition, the minutia extraction part 203 mayextract a singular point (core type singular point, delta type singularpoint) from the fingerprint image and use it as one of the minutiafeatures upon extracting minutiae.

FIG. 8 is a diagram of an example showing the minutia. FIG. 8 (a) is anexample of the minutia extracted from a part of the skeleton image asshown in FIG. 6 (a). FIG. 8 (b) is an example of the minutiae extractedfrom a part of the skeleton image as shown in FIG. 6 (b). The minutiaextraction part 203 extracts, for example, minutiae from 301 to 317 asshown in FIG. 8(a), and registers the position of each minutia and itstype (bifurcation point, end point) in the memory 12.

Further, in figures including FIG. 8, end points are shown as circlesand bifurcation points are shown as squares. Also, as to be describedlater, the minutia calculation part 204 extracts a minutia direction oneach minutia as one of minutia features, however, the display of theminutia direction is shown as straight line with bulge. For example, theminutia direction of minutia 301 (bifurcation point) is toward lowerleft as shown in FIG. 8(a).

The minutia features calculation part 204 is a means configured tocalculate the minutia features that feature the extracted minutiae. Theminutia features calculated by the minutia features calculation part 204include basic minutia feature(s), relation minutia feature(s), andconnection type(s).

The basic minutia features include position of the minutia, type ofminutia, and minutia direction that features the minutia by thedirection. The position, the type, and the minutia direction are theminutia features that provide the basis for calculating the relationminutia feature(s) and the connection type described above.

The relation minutia feature(s) is the feature that indicates arelationship between two minutiae. Hereinafter in explanation, theminutia for calculating the relation minutia feature(s) is expressed as“parent minutia”. Also, the minutia that is paired with the parentminutia, and whose relation minutia feature(s) is to be calculated, isexpressed as “child minutia”.

The relation minutia features comprise three minutia features: i.e.,contour skeleton count, child minutia relative direction, and childminutia relative position.

The contour skeleton count is a minutia feature determined by the numberof skeletons existing between a skeleton in which the parent minutiaexists and a skeleton in which the child minutia exists provided thatthe skeleton (ridge line) in the fingerprint image is regarded ascontour line (contour).

The child minutia relative direction is a minutia feature indicatingwhether a tracing direction used upon identifying the nearest neighborpoint whose details will be described later is the same direction as oropposite direction to the minutia direction of the parent minutia. Thatis, the child minutia relative direction is a minutia feature indicatingwhether the child minutia is located in the same direction as theminutia direction of the parent minutia or in the opposite direction.

The child minutia relative position is a minutia feature determined by aposition where the child minutia exists based on the tracing directionused upon identifying the nearest neighbor point. For example, in casewhere the nearest neighbor point is set as the origin of the XYcoordinate system and the tracing direction of the ridge line is set asthe positive direction of the Y-axis, if the child minutia is positionedin the first or fourth quadrant, the child minutia relative position ofthe child minutia is on the “right side”, or if the child minutia ispositioned in the second or third quadrant, the child minutia relativeposition of the child minutia is on the “left side”. That is, the childminutia relative position is the minutia feature indicating whether thechild minutia exists on the left side or the right side when the tracingdirection is upward at the nearest neighbor point.

The connection type is a minutia feature indicating a connection typebetween the following two minutiae, when the parent minutia and thechild minutia are connected by at least one skeleton.

Hereinafter, three minutia features mentioned above will be sequentiallyexplained.

FIG. 9 is a diagram of an example showing process configuration of theminutia features calculating part 204. By referring FIG. 9, the minutiafeatures calculation part 204 includes three sub-modules, comprising: abasic minutia features calculation part 211, a relation minutia featurescalculation part 212, and a connection type calculation part 213.

The basic minutia features calculation part 211 is a means configured tocalculate the position, the direction, and the type of the minutiaextracted from the skeleton image as the minutia features that featurethe extracted minutiae. In more detail, the basic minutia featurescalculation part 211 mainly calculates the minutia direction.

First, explanation on the calculation of the minutia direction for thebifurcation point is given. Here, calculation of a minutia direction 332of a bifurcation point 331 shown in FIG. 10(a) will be explained.

First, the basic minutia features calculation part 211 determines threetrace end points from 333 to 335 by tracing each of the three skeletons(tracing back on the skeleton) forming the bifurcation point 331 by apredetermined distance from the bifurcation point 331 (Ref. FIG. 10(b)).

Next, the basic minutia features calculation part 211 calculates threeangles from a1 to a3 formed (spanned) by three straight lines defined bythe bifurcation point 331 and the three trace end points from 333 to335, respectively (Ref. FIG. 10(c)).

Next, the basic minutia features calculation part 211 selects thesmallest angle among the three calculated angles, and calculates thedirection that bisects the smallest angle as the minutia direction 332(Ref. FIG. 10(b)).

Next, explanation will be given on the calculation of the minutiadirection for the end point by referring to FIG. 11.

First, the basic minutia features calculation part 211 traces apredetermined distance on the skeleton forming a trace end point from anend point 341, and calculates the trace end point 342. The basic minutiafeatures calculation part 211 calculates the direction from the endpoint 341 toward the trace end point 342, as the minutia direction ofthe end point 341.

Here, the minutia direction is expressed as a minutia feature using anangle formed (spanned) by the X axis in the XY coordinate system(two-dimensional coordinate system) in the fingerprint image and astraight line defined by the minutia direction.

The basic minutia features calculation part 211 performs the mentionedprocessing on the minutiae extracted by the minutia extraction unit 203to calculate the basic minutia features. For example, the basic minutiafeatures calculation part 211 calculates information as shown in FIG. 12as the basic minutia features. Note that Information relating to theminutia position and the minutia type of each minutia is calculatedusing the minutia information extracted by the minutia extraction part203.

The relation minutia features calculation part 212 is a means configuredto calculate the relation minutia features. Concretely, the relationminutia features calculation part 212 counts the number of intersectiontimes that a straight line connecting the nearest neighbor point withthe child minutia, the nearest neighbor point being on the trace linecorresponding to the skeleton on which the parent minutia exists, andhaving the shortest straight line distance from the child minutia,intersects with the skeleton(s) between the nearest neighbor point andthe child minutia, and determines the number of intersection times asone of the minutia features defined as “contour skeleton count”. Also,in the process of calculating the contour skeleton count, the featurevalues related to the child minutia relative direction and the childminutia relative position are calculated. Details about mentioned traceline will be explained later.

FIG. 13 is a flowchart of an example of operation of the relationminutia features calculation part 212. By referring to FIG. 13,operations on the relation minutia features calculation part 212 will beexplained.

In step S101, the relation minutia features calculation part 212determines the minutiae (the parent minutia, the child minutiae) forcalculating the relation minutia features. Concretely, the relationminutia features calculation part 212 selects a parent minutia from aplurality of minutiae, then extracts a predetermined number of minutiaein the order of proximity to the parent minutia, and sets the extractedminutia(e) as child minutia(e). Here, a case is considered where 16minutiae close to the parent minutia are set as child minutiae. In thiscase, in the example shown in FIG. 8(a), if the minutia 301 is set asthe parent minutia, the minutiae from 302 to 317 are selected as childminutiae.

Here, the number of child minutiae is an example, and it is needless tosay that the number of child minutiae to be selected is not limited.Increasing the number of child minutiae improves matching accuracy, butalso increases the load (computation amount) related to the relationminutia features calculation. Therefore, it is preferred to select anappropriate number of child minutiae in consideration of thecontribution of relation minutia features to matching accuracyimprovement and the load related to the minutia features calculationconcerned.

When the parent minutia and the child minutiae are determined, therelation minutia features calculation part 212 selects one of the childminutiae, and performs ridge line direction tracing using ridge linedirection data starting from the parent minutia. The relation minutiafeatures calculation part 212 calculates the trace line described aboveby performing ridge line direction tracing. Here, details of ridge linedirection tracing using ridge line direction data are described in theabove reference materials, and the same method as the ridge linedirection tracing described in the materials can be used, so thedescription will be made briefly as below.

For example, set an end point 351 shown in FIG. 14 as the starting pointof the ridge line direction tracing. Initially, the relation minutiafeatures calculation part 212 traces (go up) along the ridge linedirection in a predetermined direction (initial direction; for example,the same direction as the minutia direction of the end point 351) fromthe start point (that is, the end point 351). As a result of tracing, ifthe ridge line direction data is interrupted, the relation minutiafeatures calculation part 212 shifts by a predetermined distance in thesame direction as the trace direction (in case of FIG. 14(a), shiftsover a dotted line 352). As a result of the shift, when another ridgeline direction data 353 is encountered, the relation minutia featurescalculation part 212 traces along the ridge line direction data 353. Thetracing on the ridge line direction data as described above is repeated,and when the traced distance reaches a predetermined distance, the ridgeline direction tracing terminates.

In addition, when the ridge line direction tracing is performed, therelation minutia features calculation part 212 performs two directiontracing in the directions one that matches the minutia direction of theminutia that is the starting point of the tracing, and the other beingdirection of opposite to the minutia direction (a direction that isinverted by 180 degrees). The result (track) obtained by the ridge linedirection tracing in two directions performed by the relation minutiafeatures calculation part 212 becomes a trace line.

Here, when performing a ridge line direction tracing, if the initialdirection of the trace traces on (along) the skeleton, a part of atrajectory (track) obtained by the ridge line direction tracingsubstantially matches a track obtained by the skeleton tracing. Forexample, by referring to FIG. 15(a), in case an end point 421 is thestarting point, and the upper right direction which is the minutiadirection is the initial direction of the ridge line direction tracing,the track (thin solid line with arrows), obtained by the ridge linedirection tracing and trace obtained on (along) the skeleton 422, onwhich the end point 421 exists, both tracks are substantially match eachother. On the other hand, in case performing the ridge line directiontracing, if the initial direction of the tracing is not the directionshifting on (along) the skeleton, there is no direct relationshipbetween a track obtained by the ridge direction line tracing and theskeleton. For example, as shown in FIG. 15(b), in case the ridge linedirection tracing is executed in a direction opposite to the minutiadirection of the end point 421, the resultant track (thin solid linewith arrows) obtained by the tracing and the skeleton 422, both trackshave no direct relationship.

The trace line corresponds to the track of the tracing by the ridge linedirection tracing in two directions. Concretely, the thin solid linewith arrows shown in FIG. 15(a) and the thin solid line with arrowsshown in FIG. 15(b) are the trace lines. Here, as is clear from theexplanation above, the trace lines, that is, results of performing theridge line direction tracing in two directions starting from the minutiaon the skeleton includes the skeleton in a part thereof. For example, asshown in FIG. 15(c), the trace line substantially includes the skeleton422.

By referring to FIG. 14, in case the parent minutia exists at the pointQ1 as shown in FIG. 14(b), and the minutia direction is toward the upperright, tracing is performed along a route indicated by a dotted line(s),and the ridge line direction tracing terminates at the point Q2.

Note that, the ridge line direction tracing and skeleton tracing aredifferent methods. The skeleton tracing is a tracing by a predetermineddistance literally along the skeleton. Thus, in case the skeleton isinterrupted due to sweat pore and so on, the skeleton tracing isterminated at the end of the skeleton (i.e., the end point). Therefore,with the normal skeleton tracing, the trace lines explained withreference to FIG. 14 and FIG. 15 cannot be obtained. However, in casethe end point or bifurcation point is encountered during skeletontracing, the above trace lines can be obtained by tracing the valleyline by shifting the tracing one to the valley line at the bifurcationpoint (or end point) of the valley line existing in the vicinitythereof. Here, the valley line indicates a black and white invertedimage obtained by inverting the pixel values of the skeleton. That is,an image in which a ridge-line-groove (white background) is assumed asthe ridge line is a black/white inverted image. In this way, it is alsopossible to obtain the trace line by performing the skeleton linetracing using the black/white inverted image. Here, even for the traceline obtained by the skeleton tracing using the black/white invertedimage, a part thereof substantially matches the skeleton.

In step S102, the relation minutia features calculation part 212determines the direction of the ridge line direction tracing. In moredetail, the relation minutia features calculation part 212 determines afirst trace direction, by setting the minutia direction of the parentminutia as the first trace direction, and a second trace direction, bysetting the direction opposite to the minutia direction of the parentminutia (a direction obtained by rotating the minutia direction by 180degrees).

In step S103, the relation minutia features calculation part 212 setsthe parent minutia as the start point of tracing, and performs the ridgeline direction tracing in the first trace direction as an initialdirection. At that time, the relation minutia features calculation part212 determines a first nearest neighbor candidate point (point (pixel)on the trace) that is the point on the trace line and has a shortestdistance from the child minutia.

In step S104, the relation minutia features calculation part 212 setsthe parent minutia as the start point of the tracing, and performs theridge line direction tracing in the second trace direction as theinitial direction. At that time, the relation minutia featurescalculation part 212 determines a second nearest neighbor candidatepoint (point (pixel) on the trace) that is a point on the trace line andhas a shortest distance from the child minutia

Here, depending on the positional relationship between the parentminutia point and the child minutia, the parent minutia (start point)may be the first or second nearest neighbor candidate point. Also, it ispreferred to limit the trace distance in the processes of steps S103 andS104 in advance. For example, it is preferred to limit the tracedistance to be about twice of the simple distance connecting the parentminutia and the child minutia.

In step S105, the relation minutia features calculation part 212selects, as the “nearest neighbor point”, the nearest neighbor candidatepoint that is closer to the child minutia among the first and secondnearest neighbor candidate points.

In addition, the relation minutia features calculation part 212determines a “child minutia relative direction” based on the tracedirection used to calculate the nearest neighbor point.

Concretely, in case the nearest neighbor point is obtained in the firsttrace direction, the relation minutia features calculation part 212assumes that a child minutia exists in the same direction as the featurepoint direction of the parent minutia, and sets a feature “FORWARD” asthe child minutia relative direction. On the other hand, in case thenearest neighbor point is obtained in the second trace direction, therelation minutia features calculation part 212 assumes that the childminutia exists in the opposite direction to the minutia direction of theparent minutia, and sets a feature “BACKWARD” (opposite direction) asthe child minutia relative direction.

In step S106, the relation minutia features calculation part 212 sets astraight line between the nearest neighbor point and the child minutia,and calculates the number of intersections of the straight line with theskeleton, which becomes a value of “contour skeleton count”.

In step S107, the relation minutia features calculation part 212determines whether the calculated contour skeleton count is 1 or more.

If the contour skeleton count is “1” or more (in step S107, “YES”branch), the relation minutia features calculation part 212 operates theprocess of step S108. If the contour skeleton count is “0” (in stepS107, “NO” branch), the relation minutia features calculation part 212operates the process of step S109 and following steps.

In step S108, the relation minutia features calculation part 212 theminutia feature related to “child minutia relative position”. Asexplained above, the relation minutia features calculation part 212 setsthe nearest neighbor point to the origin of the XY coordinate axis andthe direction of the ridge line direction trace to the positivedirection of the Y axis, and determines the “child minutia relativeposition” according to the quadrant in which the child minutia exists.

Referring to FIG. 16, for example, in the XY coordinate system in whichthe nearest neighbor point 361 is the origin and the direction of theridge line direction tracing is the positive direction of the Y axis,the child minutia 362 is located in a first quadrant, so the childminutia relative position related to the minutia 362 is “RIGHT”.

As explained above, in case the contour skeleton count is “0” (in stepS107, “NO” branch), the relation minutia features calculation part 212operates the processes of step S109 and following steps.

In step S109, the relation minutia features calculation part 212determines whether or not the parent minutia and the child minutia aredirectly connected by one or more skeleton.

In case the parent minutia and the child minutia are not directlyconnected by one or more skeleton (in step S107, “NO” branch), therelation minutia features calculation part 212 terminates the process.

In case the parent minutia and the child minutia are directly connectedby one or more skeletons (in step S107, “YES” branch), the relationminutia features calculation part 212 operates the process of step S110.

In step S110, the relation minutia features calculation part 212 startsthe connection type calculation part 213. Concretely, the relationminutia features calculation part 212 notifies the connection typecalculation part 213 of information (identifier or coordinate positionfor identifying the minutiae) regarding the parent minutia and the childminutia whose contour skeleton count is “0”, and instruct to start thecalculation of “connection type”. Details regarding the calculation ofthe “connection type” by the connection type calculation part 213 willbe described later. Here, in case the contour skeleton count is “0”,since the distance between the nearest neighbor point and the childminutia is short, and the child minutia relative position is opted tovary, it is preferred to set the child minutia relative position featureof the child minutia as “UNKNOWN”. Also, in case the nearest neighborpoint and the child minutia match, since the child minutia relativeposition cannot be calculated, the feature concerned becomes “UNKNOWN”.

Next, the calculation of the relation minutia features by the relationminutia features calculation part 212 will be concretely described withreference to the figures. Here, the calculation of the relation minutiafeatures, in case the minutia 301 shown in FIG. 8(a) is the parentminutia and the minutia 304 is the child minutia, will be described.

Referring to FIG. 17A, since the minutia direction of a parent minutia371 (minutia 301) is toward lower left direction, the first tracedirection is the lower left direction, and the second trace direction istoward upper right direction. When the ridge line direction tracing isperformed in the first trace direction (shown by a thin dotted line),the distance between the trace point and the child minutia 372 (minutia304) is increasing and increasing, so that the first nearest candidatepoint is the same position as the parent minutia 371.

Next, when the ridge line direction tracing is performed in the secondtrace direction (shown by a thick dotted line), the position of theblack point in the figure is calculated as a position whose distancefrom the child minutia 372 is the shortest. Thus, the second nearestcandidate point is the black point in the figure.

Comparing a distance between the first nearest neighbor candidate point(parent minutia 371) and the child minutia 372 and a distance betweenthe second nearest neighbor candidate point (black point) and the childminutia 372, the latter is shorter, so that the black point becomes thenearest neighbor point 373. Since the nearest neighbor point 373 isobtained by the second trace direction, the “child minutia relativedirection” regarding the child minutia 372 becomes “BACKWARD”.

Further, since the number of skeletons intersecting with a straight lineconnecting the nearest neighbor point 373 and the child minutia 372 is“3”, the contour skeleton count related to the child minutia 372 becomes“3”.

Note that skeleton lines that are not from true ridge lines may appearin the skeleton image due to noise (for example, incipient ridges). Inorder to eliminate the influence of such noise, it is preferred to takemeasures such as not to add (count) skeleton(s) existing within apredetermined distance from the nearest neighbor point.

Also, as for the child minutia relative position with respect to thechild minutia 372, as shown in the example of FIG. 17(a), if thepositive direction of the Y axis is set as the ridge line directiontrace at the nearest neighbor point 373, the child minutia 372 exists inthe fourth quadrant, resulting in that the child minutia relativeposition related to the child minutia 372 becomes “RIGHT”.

Here, in the fingerprint image in FIG. 17(a) is related to thefingerprint image (impressed fingerprint) in FIG. 8(a). Also, for thefingerprint image (latent fingerprint) shown in FIG. 8(b), the relationminutia features are similarly calculated by the matching apparatus 30(see FIG. 17(b)).

Referring to FIG. 17(b), the contour skeleton count of the parentminutia 381 relating to the child minutia 382 is “3”, the child minutiarelative direction is “BACKWARD”, and the child minutia relativeposition is “RIGHT”. The “relation minutia features” calculated from theimpressed fingerprint and the latent fingerprint are the same, whichmeans the relation minutia features are not affected by imagedistortion.

Here, confirming with the FIG. 17(a), since the parent minutia 371 andthe child minutia 374 (minutia 305) on the fingerprint exists on thesame skeleton, the “contour skeleton count” related to the child minutia374 becomes “0”. Similarly, confirming with FIG. 17(b), since the parentminutia 381 and the child minutia 383 on the latent fingerprint exist onthe same skeleton, the “contour skeleton count” related to the childminutia 383 also becomes “0”. As described above, in case the contourskeleton count is “0”, the child minutia relative position of the childminutia is not calculated (set to UNKNOWN).

The relation minutia features calculation part 212 repeats the abovedescribed processing for each element of configured child minutiae, andcalculates the relation minutia features of the child minutia related toone parent minutia (child minutia relative direction, child minutiarelative position, contour skeleton count). Also, the relation minutiafeatures calculation part 212 repeats the above processing by switchingthe parent minutia to another minutia, and calculates the relationminutia features related to the minutiae in the fingerprint image.

For example, the relation minutia features calculation part 212calculates information as shown in FIG. 18 as the relation minutiafeatures.

Note that, in disclosure of the present application, the skeleton andthe ridge line can be treated also as synonymous. In particular, withrespect to the “relation minutia features”, the skeleton and the ridgeline can be treated as synonymous. For example, the relation minutiafeatures calculation part 212 calculates the number of ridge linesexisting between the child minutia and the nearest neighbor point, andmay calculate the “contour ridge line count” as a minutia featureinstead of the “contour skeleton count”.

Next, explanation on the connection type calculation part 213 follows.

The connection type calculation part 213 is started in case the contourskeleton count is calculated as “0” in the calculation of the relationminutia features, and the parent minutia and the child minutia aredirectly connected by one or more skeletons. Note that the fact that theparent minutia and the child minutia are directly connected by one ormore skeletons represents that no other minutia exists between theparent minutia and the child minutia. In other words, in case anotherminutia exists between the minutia and the child minutia on the sameskeleton, the connection type for the child minutia concerned is notcalculated. Further, in case the child minutia exists on the trace lineobtained by the ridge line direction tracing, the “contour skeletoncount” for the child minutia concerned is “0”. Even in this case, incase the child minutia exists on the skeleton where the parent minutiaexists and is not directly connected [to the parent minutia], the childminutia is not subject to the connection type calculation. As for anexample shown in FIG. 17, although a child minutia 375 exists on thetrace line (thick dotted line) of the parent minutia 371, the childminutia 375 is not directly connected to the skeleton of the parentminutia 371, so that it is not subject to the connection typecalculation.

Prior to explaining the detailed operation of the connection typecalculation part 213, definition is given to the three skeletonsconfiguring the bifurcation point. As described with reference to FIG.10, in case the minutia is a bifurcation point, three inner angles canbe defined. Concretely, as shown in FIG. 19(a), three inner angles a1,a2, and a3 are obtained from the three skeletons forming the bifurcationpoint. Among the three inner angles, following explanation is givenprovided that the minimum inner angle is a1. Also, among three skeletonsthat form the bifurcation point, a skeleton not related to the minimuminner angle a1 (do not form inner angle a1; is not in contact to innerangle a1) is denoted as d1, the skeleton not related to the inner anglea2 as d2, and the skeleton not related to the inner angle a3 as d3.Further, as explained above, in case the minutia is the bifurcationpoint, the minutia direction (bifurcation point direction) is adirection that bisects the minimum inner angle a1 formed by theskeletons d2 and d3.

In addition, a two-dimensional coordinate system is configured in whichthe minutia position is set as the origin and the minutia direction isset as the positive Y-axis direction, and the direction is expressed byunit of 360 degrees (Ref. FIG. 19(b)). In the normal region of thefingerprint image excluding the vicinity of the core region and thelike, the skeleton d2 is located between 0 degrees and less than 90degrees (first quadrant) in the configured two-dimensional coordinatesystem. In the following explanation, the skeleton d2 located in thefirst quadrant of the configured two-dimensional coordinate system isdenoted as a right skeleton. Also, the skeleton d3 is normally locatedbetween 90 degrees and less than 180 degrees (second quadrant). Thus,the skeleton d3 located in the second quadrant of the configuredtwo-dimensional coordinate system is denoted as a left skeleton.Further, the skeleton d1 is normally located between 180 degrees andless than 360 degrees (third and fourth quadrants). Thus, the skeletond1 located in third and fourth quadrants of the configuredtwo-dimensional coordinate system is expressed as an opposite sideskeleton.

Further, in a region different from the normal region, such as thevicinity of the core region, a special skeleton form as shown in FIG.19(c) may appear instead of the skeleton form as shown in FIG. 19(b). Asshown in FIG. 19(c), in case the skeleton d1 is located in the firstquadrant or second quadrant (between 0 degrees or more and less than 180degrees) of configured two-dimensional coordinate system, the skeletond1 is expressed as a special opposite side skeleton.

In the two-dimensional coordinate system as shown in FIG. 19(b) and FIG.19(c), the minutia direction is always approximately 90 degrees.

FIG. 20 and FIG. 21 are a flowchart showing an example of operation ofthe connection type calculation part 213.

The connection type calculation part 213 determines the connection type,based on the direction and type of each of the parent minutia and thechild minutia, and the number of skeletons connecting these minutiae,and based on the type of the skeleton, which is, in case the type of atleast one of the parent minutia and the child minutia is the bifurcationpoint, the one connected to the other minutia among the three skeletonsforming the bifurcation point concerned. In more detail, the connectiontype calculation part 213 determines the type of each of the threeskeletons (the right skeleton, the left skeleton, the opposite skeleton,and the special opposite skeleton) according to any quadrant in whicheach of the three skeletons forming the bifurcation point exists, in thecoordinate system described with reference to FIG. 19 (thetwo-dimensional coordinate system in which the position of thebifurcation is set as the origin and the minutia direction featuring thebifurcation point is set as the Y axis positive direction), and make useof it for the calculation of the connection type.

First, the connection type calculation part 213 determines the type ofthe parent minutia (step S201).

In case the parent minutia is the end point (in step S201, YES branch),the processings of step S202 et seq. are to be executed. In case theparent minutia is the bifurcation point (in step S201, NO branch), theprocessings of step S211 et seq. are to be executed.

In step S202, the connection type calculation part 213 determines thetype of child minutia.

In case the child minutia is the end point (in step S202, YES branch),the connection type calculation part 213 sets the connection type of thechild minutia to “TYPE1” (step S203). For example, the connection typeas shown in FIG. 22(a) corresponds to “TYPE1”. Hereinafter, in theexplanation on FIG. 22 et seq., the parent minutia is denoted as P, andthe child minutia is denoted as C.

In case the child minutia is the bifurcation point (step S202, NObranch), the connection type calculation part 213 determines what typeof the skeleton is connected to the parent minutia among the threeskeletons that form child minutia (bifurcation point) (step S204).

In case the parent minutia is connected to the opposite side skeleton(s)of the child minutia (bifurcation point), the connection typecalculation part 213 sets the connection type of the child minutia to“TYPE2” (step S205). For example, the connection type as shown in FIG.22(b) corresponds to “TYPE2”.

In case the parent minutia is connected to a left side skeleton of thechild minutia (bifurcation point), the connection type calculation part213 sets the connection type of the child minutia to “TYPE3” (stepS206). For example, the connection type as shown in FIG. 22(c)corresponds to “TYPE3”.

In case the parent minutia is connected to a right side skeleton of thechild minutia (bifurcation point), the connection type calculation part213 sets the connection type of the child minutia to “TYPE4” (stepS207). For example, the connection type as shown in FIG. 22(d)corresponds to “TYPE4”.

As described above, in case the parent minutia is the bifurcation point(step S201, NO branch), processes of S211 et seq. are to be executed.

In step S211, the connection type calculation part 213 determines thetype of the child minutia.

In case the child minutia is the end point (step S211, YES branch), theconnection type calculation part 213 determines what type of theskeleton is connected to the child minutia among the three skeletonsthat form parent minutia (bifurcation point) (step S212).

In case the child minutia is connected to the opposite side skeleton ofthe parent minutia (bifurcation point), the connection type calculationpart 213 sets the connection type of the child minutia to “TYPE5” (stepS213). For example, the connection type as shown in FIG. 23(a)corresponds to “TYPE5”.

In case the child minutia is connected to the left side skeleton of theparent minutia (bifurcation point), the connection type calculation part213 sets the connection type of the child minutia to “TYPE6” (stepS214). For example, the connection type as shown in FIG. 23(b)corresponds to “TYPE6”.

In case the child minutia is connected to the right side skeleton of theparent minutia (bifurcation point), the connection type calculation part213 sets the connection type of the child minutia to “TYPE7” (stepS215). For example, the connection type as shown in FIG. 23(c)corresponds to “TYPE7”.

In case the type of the child minutia is the bifurcation point (stepS211, NO branch), processes of S221 et seq. are to be executed.

The explanation will be made by referring to FIG. 21, on processes ofstep S221 et seq.

In step S221, the connection type calculation part 213 determineswhether the parent minutia has the special opposite skeleton.

In case the parent minutia does not have the special opposite skeleton(step S221, NO branch), processes of the step S222 et seq. are to beexecuted. In case the parent minutia has the special opposite skeleton(step S221, YES branch), step S241 is to be executed.

In step S222, the connection type calculation part 213 determineswhether the parent minutia is connected to the minutia by one skeleton,or by two skeletons.

In case the parent minutia is connected to the minutia by one skeleton(step S222, YES branch), the connection type calculation part 213determines whether the child minutia (bifurcation point) is connected tothe parent minutia by the opposite skeleton (step S223).

In case the child minutia is not connected to parent minutia by theopposite skeleton (step S223, NO branch), the connection typecalculation part 213 determines whether the child minutia is connectedto the parent minutia (bifurcation point) by the left skeleton (stepS224).

In case the child minutia is connected to the parent minutia by the leftside skeleton (step S224, YES branch), the connection type calculationpart 213 sets the connection type of the child minutia to “TYPE8” (stepS225). For example, two connection types as shown in FIG. 24(a)corresponds to “TYPE8”.

In case the child minutia is connected to the parent minutia by theright side skeleton (step S224, NO branch), the connection typecalculation part 213 sets the connection type of the child minutia to“TYPE9” (step S226). For example, two connection types as shown in FIG.24(b) corresponds to “TYPE9”.

In case the child minutia is connected to parent minutia by the oppositeside skeleton (step S223, YES branch), the connection type calculationpart 213 determines what type of the skeleton is connected to the parentminutia among the three skeletons that form the child minutia(bifurcation point) (step S227).

In case the parent minutia is connected to the left side skeleton of thechild minutia (bifurcation point), the connection type calculation part213 sets the connection type of the child minutia to “TYPE10” (stepS228). For example, the connection type as shown in FIG. 25(a)corresponds to “TYPE10”.

In case the parent minutia is connected to the right side skeleton ofthe child minutia (bifurcation point), the connection type calculationpart 213 sets the connection type of the child minutia to “TYPE11” (stepS229). For example, the connection type as shown in FIG. 25(b)corresponds to “TYPE11”.

In case the parent minutia is connected to the opposite side skeleton ofthe child minutia (bifurcation point), the connection type calculationpart 213 sets the connection type of the child minutia to “TYPE12” (stepS230). For example, the connection type as shown in FIG. 25(c)corresponds to “TYPE12”.

In case the parent minutia is connected to two skeletons of the childminutia (step S222, NO branch), the connection type calculation part 213sets the connection type of the child minutia to “TYPE13” (step S231).For example, the connection type as shown in FIG. 25(d) corresponds to“TYPE13”.

As described above, in case parent minutia has the special oppositeskeleton (step S221, YES branch), process of S241 is to be executed.

In step S241, the connection type calculation part 213 determines theconnection type of the child minutia based on the count (number) of theskeletons connecting two minutiae.

In case the parent minutia is connected to the child minutia by oneskeleton, the connection type calculation part 213 sets the connectiontype of the child minutia to “TYPE14”. For example, the connection typeas shown in FIG. 26(a) corresponds to “TYPE14”.

In case the parent minutia is connected to the child minutia by twoskeletons, the connection type calculation part 213 sets the connectiontype of the child minutia to “TYPE15”. For example, the connection typeas shown in FIG. 26(b) corresponds to “TYPE15”.

In case the parent minutia is connected to the child minutia by threeskeletons, the connection type calculation part 213 sets the connectiontype of the child minutia to “TYPE16”. For example, the connection typeas shown in FIG. 26(c) corresponds to “TYPE16”.

In the example described above in FIG. 17(a), in case the minutia 301 isset as the parent minutia 371 and the minutia 305 is set as the childminutia 374, respectively, the contour skeleton count of the childminutia 374 becomes “0”. Thus, the connection type between the parentminutia 371 and the child minutia 374 is to be determined.

In this case, the parent minutia 371 is the bifurcation point (stepS201, YES branch), and the child minutia 374 is also the bifurcationpoint (step S221, NO branch). Also, the parent minutia does not have thespecial opposite skeleton (step S221, NO branch), and two minutiae areconnected by one skeleton (step S222, YES branch). Further, the childminutia 374 is connected to the opposite side skeleton of the parentminutia 371 (step S223, YES branch), and the parent minutia is connectedto the opposite side skeleton of the child minutia. Thus, the connectiontype between the parent minutia 371 and the child minutia 374 isdetermined as “TYPE12” (step 230).

As explained above, the connection type calculation part 213 determinesthe type of connection, based on the type of minutia (parent minutia,child minutia), the type of skeleton forming the bifurcation point (leftside skeleton, right side skeleton, opposite side skeleton, specialopposite skeleton), and the count of the skeleton connecting between theparent minutia and the child minutia. However, the connection typecalculation part 213 may not only determine the connection type based onthe type of the skeleton etc., but also determine based on the position,the minutia direction, and the type of each of the parent minutia andthe child minutia (that is, the basic minutia features), and the numberof skeleton(s) connecting two minutiae.

FIG. 27 and FIG. 28 show an example of other operation of the connectiontype calculation part 213.

First, the connection type calculation part 213 determines the type ofthe parent minutia (step S301).

In case the parent minutia is the end point (step S301, YES branch),processes of step S302 et seq. are to be executed. In case the parentminutia is the bifurcation point (step S301, NO branch), processes ofstep S311 et seq. are to be executed.

In step S302, the connection type calculation part 213 determines thetype of the child minutia.

In case the child minutia is the end point (step S302, YES branch), theconnection type calculation part 213 sets the connection type of thechild minutia to “TYPE31” (step S303). For example, the connection typeas shown in FIG. 22(a) corresponds to “TYPE31”.

In case the child minutia is the bifurcation point (step S302, NObranch), the connection type calculation part 213 determines whether theminutia direction of the parent minutia is the same as the minutiadirection of the child minutia (step S304). Note that, the determinationwhether or not the minutiae directions are the same is determined asfollows. That is, in case the angle difference Δ between the two minutiadirections is within a predetermined range (0 degrees≤Δ≤90 degrees, −90degrees≤Δ<0 degrees) the directions are the same, and in case out of thepredetermined range (90 degrees<Δ≤180 degrees, −90 degrees<Δ<180degrees) the directions are the opposite.

In case two minutia directions are the same (step S304, YES branch), theconnection type calculation part 213 sets the connection type of thechild minutia to “TYPE32” (step S305). For example, the connection typeas shown in FIG. 22(b) correspondents to “TYPE32”.

In case two minutia directions are the opposite (step S304, NO branch),the connection type calculation part 213 determines whether the minutiadirection of the child minutia is directed to left side (first quadrant)or right side (fourth quadrant) as seen from the minutia direction ofthe parent minutia (step S306).

In case directed to left side (step S306, YES branch), the connectiontype calculation part 213 sets the connection type of the child minutiato “TYPE33” (step S307). For example, the connection type as shown inFIG. 22(c) corresponds to “TYPE33”.

In case directed to right side (step S306, NO branch), the connectiontype calculation part 213 sets the connection type of the child minutiato “TYPE34” (step S308). For example, the connection type as shown inFIG. 22(d) corresponds to “TYPE34”.

As described above, in case the parent minutia is the bifurcation point(step S301, NO branch), processes of step S311 et seq. are to beexecuted.

In step S311, the connection type calculation part 213 determines thetype of child minutia.

In case the type of the child minutia is the end point (step S311, YESbranch), the connection type calculation part 213 determines whether theminutia direction of the parent minutia is the same as the minutiadirection of the child minutia (step S312). In case the minutiadirections are the same direction (step S312, YES branch), theconnection type calculation part 213 sets the connection type of thechild minutia to “TYPE35” (step S313). For example, the connection typeas shown in FIG. 23(a) corresponds to “TYPE35”.

In case the minutia directions are the opposite direction (step S312, NObranch), the connection type calculation part 213 determines whether thechild minutia is located in first quadrant or fourth quadrant, whenconfigured the XY coordinate system as the parent minutia being theorigin and the minutia direction of the parent minutia is the positivedirection of the X axis (step S314). In case the child minutia islocated in the first quadrant (that is, exists on the left side seenfrom the parent minutia), the connection type calculation part 213 setsthe connection type of the child minutia to “TYPE36” (step S315). Forexample, the connection type as shown in FIG. 23(b) corresponds to“TYPE36”.

In case the child minutia is located in the fourth quadrant (that is,exists right side seen from the parent minutia), the connection typecalculation part 213 sets the connection type of the child minutia to“TYPE37” (step S316). For example, the connection type as shown in FIG.23(c) corresponds to “TYPE37”.

In case the type of the child minutia is the bifurcation point (stepS311, NO branch), processes of step S321 et seq. are to be executed.

The explanation will be made by referring to FIG. 28, on processes ofstep S321 et seq.

In step S321, the connection type calculation part 213 determineswhether the parent minutia and the child minutia are connected by oneskeleton.

In case the parent minutia and the child minutia are connected by oneskeleton (step S321, YES branch), the connection type calculation part213 determines whether the minutia direction of the parent minutia isthe same as the minutia direction of the child minutia (step S322).

In case the two minutia directions are the same (step S322, YES branch),the connection type calculation part 213 determines whether the childminutia is located in the first quadrant or fourth quadrant, whenconfigured the XY coordinate system as the parent minutia being theorigin and the minutia direction of the parent minutia is set as thepositive direction of the X axis (step S323).

In case the child minutia is located in first quadrant (that is, existson the left side seen from the parent minutia), the connection typecalculation part 213 sets the connection type of the child minutia to“TYPE38” (step S324). For example, the connection type as shown in FIG.24(a) corresponds to “TYPE38”.

In case the child minutia is located in fourth quadrant (that is, existson the right side seen from the parent minutia), the connection typecalculation part 213 sets the connection type of the child minutia to“TYPE39” (step S325). For example, the connection type as shown in FIG.24(b) corresponds to “TYPE39”.

In case two minutia directions are the opposite (step S322, NO branch),the connection type calculation part 213 determines whether or not twominutia directions are opposite (step S326). Note that the determinationon whether or not two minutiae are opposite can be found by thefollowing. For example, when the parent minutia is set as the origin ofthe XY coordinate system and the minutia direction of the parent minutiais set as the positive direction of the Y axis, by shifting the childminutia parallel to the X axis direction, find whether or not childminutia can be shifted to the positive Y-axis in the positive region.

In case two minutia directions are not opposite (step S326, NO branch),the connection type calculation part 213 sets the connection type of thechild minutia to “TYPE40” (step S327). For example, the connection typeas shown in FIG. 25(c) corresponds to “TYPE40”.

In case two minutia directions are opposite (step S326, YES branch), theconnection type calculation part 213 determines existing quadrant ofchild minutia, by setting the parent minutia being the origin of the XYcoordinate system and by setting the minutia direction of the parentminutia as the positive direction of the Y axis, as the location ofchild minutia in first quadrant or fourth quadrant (step S328). In casethe child minutia is located in first quadrant (that is, exists on theleft side seen from the parent minutia), the connection type calculationpart 213 sets the connection type of the child minutia to “TYPE41” (stepS329). For example, the connection type as shown on the left side ofFIG. 24(a) corresponds to “TYPE41”.

In case the child minutia is located in fourth quadrant (that is, existson the right side seen from the parent minutia), the connection typecalculation part 213 sets the connection type of the child minutia to“TYPE42” (step S330). For example, the connection type as shown on theleft side of FIG. 24(b) corresponds to “TYPE42”.

In case the parent minutia and the child minutia are connected by two ormore skeletons (step S321, NO branch), the connection type calculationpart 213 determines the connection type of child minutia based on thenumber (count) of skeletons connecting two minutiae (step S331).

Concretely, in case two minutiae are connected by two skeletons, theconnection type calculation part 213 sets the connection type of thechild minutia to “TYPE43”. For example, the connection type as shown inFIG. 25(d) or FIG. 26(b), corresponds to “TYPE43”.

In case two minutiae are connected by three skeletons, the connectiontype calculation part 213 sets the connection type of the child minutiato “TYPE44”. For example, the connection type as shown in FIG. 26(c),corresponds to “TYPE44”.

In the example of FIG. 17A described above, in case the minutia 301 isset as the parent minutia 371 and the minutia 305 is set as the childminutia 374, the contour skeleton count of the child minutia 374 becomes“0”. Therefore, the connection type between the parent minutia 371 andthe child minutia 374 is determined.

In this case, the parent minutia 371 is the bifurcation point (stepS301, NO branch), and the child minutia 374 is also the bifurcationpoint (step S311, NO branch). In addition, these minutiae are connectedby one skeleton (step S321, YES branch), and the minutia direction isopposite (step S322, NO branch). In addition, two minutiae are notopposite (step S326, NO branch). Thus, the connection type between theparent minutia 371 and the child minutia 374 is determined as “TYPE40”.

Here, the connection type calculation part 213 may perform theconnection type determination process (determination process using theconnection type of the skeleton) according to FIG. 20 and FIG. 21, andthe connection type determination process according to FIG. 27 and FIG.28 (determination process using the positions and minutia directions ofeach of two minutia) by combination. For example, in the formerdetermination, both of two connection types shown in FIG. 24(a) aredetermined as “TYPE8”. Therefore, in case it is determined as “TYPE8” inthe former determination process, the latter determination may beperformed to distinguish the two connection types. In more detail, byusing the latter determination process, the right side of FIG. 24(a) isdetermined as “TYPE38” and the left side is determined as “TYPE41”.

Hereinabove, the operation of the minutia features calculation part 204are described.

Returning to FIG. 5, the minutia features output part 205 outputs, tothe database 20, various minutia features (basic minutia features,relation minutia features, connection type) calculated by the minutiafeatures calculation part 204 together with the fingerprint image IDinformation and the fingerprint image. For example, the minutia featuresoutput part 205 outputs the minutia features as shown in FIG. 29 to thedatabase 20.

[Matching Apparatus]

Next, a configuration and an operation(s) of the matching apparatus 30will be explained.

FIG. 30 is a diagram showing an example of a process configuration ofthe matching apparatus 30 according to the first exemplary embodiment.By referring to FIG. 30, the matching apparatus 30 is configured,comprising a fingerprint image input part 221, a skeleton imagegeneration part 222, a minutia extraction part 223, a minutia featurescalculation part 224, a database (DB) access part 225, a matching part226, and collation result output unit 227.

Since each of the fingerprint image input part 221, the skeleton imagegeneration part 222, the minutia extraction part 223, and the minutiafeatures calculation part 224 can be the same as each of thecorresponding processing modules of the minutia features generationapparatus 10, the descriptions thereof are omitted.

As described above, the fingerprint image input to the matchingapparatus 30 is assumed to be the fingerprint image (latent fingerprint)as shown in FIG. 30. Also, assume that the matching apparatus 30operates the minutia features calculation part 224 and the like, andcalculates the minutia features as shown in FIG. 31 from the fingerprintimage (latent fingerprint) shown in FIG. 3(c).

The database access unit 225 is a means configured to access thedatabase 20 and acquire the fingerprint image, the minutia features, andthe ID information stored in the database. Here, it is assumed that thefingerprint image (impressed fingerprint) shown in FIG. 3(a) and theminutia features shown in FIG. 29 are acquired.

The matching part 226 is a means configured to perform matchingprocessing between the input fingerprint image (latent fingerprint) andthe fingerprint image (impressed fingerprint) whose minutia features arestored in the database 20.

FIG. 32 is a diagram showing an example of an operation of the matchingpart 226.

First, the matching part 226 calculates a paired minutiae candidate fromthe minutia included in the search side fingerprint image (latentfingerprint) and the file side fingerprint image (impressed fingerprint)(step S401). Concretely, the matching part 226 performs alignment(positioning) processing of two fingerprint images and converts minutiacoordinate(s).

Various methods can be used to align the two fingerprint images. Forexample, two fingerprint images may be aligned using singular points(core singular point, delta singular points).

In case two minutia coordinates of the search side and the file side arewithin a predetermined range after the coordinate conversion in thealignment processing, the matching part 226 treats the two minutiae asthe paired minutiae candidate. After that, the matching part 226compares the minutia features of the paired minutiae candidate andperforms a final determination (paired minutiae, non-paired minutia).

For example, referring to FIG. 8, the matching part 226 searches theminutia candidate(s) paired with the search side minutia 321 shown inFIG. 8(b), from the file side minutiae 301 to 307 shown in FIG. 8(a). Inthe example of FIG. 8, the minutia 301 is calculated as the pairedminutia candidate with the minutia 321.

Next, the matching part 226 compares the minutia features of the minutia321 (the minutia features from the first to fifth line records in FIG.31) with the minutia features of the minutia 301 (the minutia featuresfrom the first to seventh line records in FIG. 29). The matching part226 determines whether or not these minutiae are the paired minutiaebased on the comparison result. Concretely, the matching part 226compares each element of the minutia features (basic minutia features,relation minutia features, connection type) that feature two minutiae,and increases the matching score in case they match. On the other hand,the matching part 226 lowers the matching score in case there are nominutia features corresponding to the two minutiae, or in case eachelement of the corresponding minutia features does not match.Alternatively, the matching part 226 may change the matching score(s) byweighting each element.

The matching part 226 performs threshold processing for the matchingscore(s) calculated as described above, and determines whether or notthe two minutiae are the paired minutiae. According to the example ofFIG. 29 and FIG. 31, since the search side fingerprint image (latentfingerprint) has only a partial area, the relation minutia features andthe like of the child minutia 307 to 317 do not exist in the fingerprintimage on the search side (latent fingerprint; FIG. 31). However, sincethe relation minutia features related to the child minutia 302 to 306and the child minutiae 322 to 326 corresponding to the child minutiamatch, the matching score(s) (reliability) of the two minutiae, whichare the paired minutiae 311 and 301, are calculated to be high. Theminutia 301 and the minutia 321 are determined as the paired minutiae.

By repeating above determination for the other minutiae, the pairedminutiae shown in FIG. 33 are calculated out. In FIG. 33, the pairedminutiae of the file side (impressed fingerprint; the fingerprint imageas shown in FIG. 6(a)) and the search side (latent fingerprint; thefingerprint image as shown in FIG. 6(b)) are connected by a solid line,explicitly.

After the calculation of the paired minutiae, the matching part 226calculates the matching score relating to the fingerprint image (subjector target of matching processing performed) acquired from the database20 (step S402 in FIG. 32). The matching score is an index indicating thesimilarity (matching degree) between the fingerprint image of the searchside (latent fingerprint) and the fingerprint image of the file side(impressed fingerprint).

The matching part 226 also calculates the matching score(s) using theminutia features calculated from each fingerprint image in thecalculation of the matching score(s). For example, in case the number ofminutiae determined as the paired minutiae is a lot, the matching part226 increases the matching score of the fingerprint image concerned(file side fingerprint image, impressed fingerprint). Alternatively, incase one fingerprint image has a minutia but the other fingerprint imagedoes not have a corresponding minutia, the matching part 226 lowers thematching score of the fingerprint image.

The matching part 226 repeats such processing for the fingerprint image(impressed fingerprint) acquired from the database 20, and calculatesthe matching scores for each fingerprint image (impressed fingerprint)on the file side.

The matching result output part 227 selects and outputs fingerprintimages having high matching score(s) calculated by the matching part 226according to a predetermined rule. For example, the matching resultoutput part 227 selects a predetermined number of fingerprint images indescending order of the matching scores. Alternatively, the matchingresult output part 227 performs threshold processing for the matchingscores and selects fingerprint images having a matching score higherthan a predetermined value.

The matching result output part 227 outputs a combination of the IDinformation of the selected fingerprint image and the matching score(s)as the matching result. For example, the matching result output part 227prints the matching result as shown in FIG. 34, displays it on amonitor, and outputs it to an external storage device such as a USB.Alternatively, the matching result output part 227 may output not onlythe matching scores but also the minutia features (minutia features asshown in FIG. 29 and FIG. 31) that are the basis (foundation) of thematching score calculation, to external devices (a printer, a monitor,an external storage device, and so on).

FIG. 35 is a diagram showing an example of the operation of thefingerprint matching system according to the first exemplary embodiment.

The minutia features generation apparatus 10 inputs a fingerprint imagethat is a target of minutia features calculation such as an impressedfingerprint (step S01). Then, the minutia features generation apparatus10 generates a skeleton image from the fingerprint image (step S02), andextracts minutiae (step S03). And the minutia features generationapparatus 10 calculates various minutia features (basic minutiafeatures, relation minutia features, connection type) using the skeletonimage and ridge line direction data, and the like (step S04).Thereafter, the minutia features generation apparatus 10 outputs thefingerprint image whose minutia features are calculated, the minutiafeatures of the fingerprint image concerned, and ID information to thedatabase 20 (step S05).

The database 20 stores the minutia features and so on from the minutiafeatures generation apparatus 10 in a storage device (step S11).

The matching apparatus 30 inputs a fingerprint image (latentfingerprint) that is a target of fingerprint matching (step S21). Then,the matching apparatus 30 calculates various minutia features in thesame manner as the minutia features generation apparatus 10 (steps S22to S24). Then the matching apparatus 30 acquires information (thefingerprint image, the minutia features, the ID information) stored inthe database 20 (step S25), and performs matching processing (step S26).Then, the matching apparatus 30 outputs the matching results to anexternal device(s) (step S27).

Note that, the configuration of the fingerprint matching systemdescribed in the above embodiment is an example, and is not intended tolimit the configuration of the system. For example, the function of thematching apparatus 30 may be incorporated in the minutia featuresgeneration apparatus 10. Alternatively, the minutia features generationapparatus 10 may be implemented as a part of the functions of thematching apparatus 30, or the functions of the database 20 may beimplemented as a part of the functions of the matching apparatus 30.

Alternatively, the matching apparatus 30 may input two images related tothe impressed fingerprint and the latent fingerprint, and match thesefingerprint images (a match or a mismatch may be determined).

In the above exemplary embodiment, the contour skeleton count betweentwo minutiae is calculated as the number of skeletons, however, inaddition to the contour skeleton count, the conventional ridge linecount (number of skeletons) between minutia as disclosed in Non PatentLiterature 2 and 3 may also be calculated as a minutia feature and usedfor the matching processing.

In the above exemplary embodiment, the origin of the XY coordinatesystem is set at the nearest neighbor point, and the child minutiarelative position is calculated. In addition to the minutia feature (oras an alternative), by setting the parent minutia as the origin of theXY coordinate system, minutia features determined by a position, atwhich the child minutia exists based on the trace direction used uponidentifying the nearest neighbor point may be calculated. Moreconcretely, a minutia feature indicating whether the child minutia islocated on the right side or the left side on an extension line of thetrace direction may be calculated. Concretely, suppose that the parentminutia is set as the origin of the XY coordinate system and the tracedirection of the ridge line is set as the positive direction of the Xaxis, respectively, when the child minutia is located in first quadrant,the feature value of the child minutia becomes “LEFT SIDE”, or in casethe child minutia is located in fourth quadrant, the minutia feature ofthe child minutia becomes “RIGHT SIDE”.

In the above exemplary embodiment, although the configuration and theoperation of the minutia features generation apparatus 10 and the likehave been described using the fingerprint image as the image in whichthe curved stripe pattern is formed by ridge lines, the image to behandled is not limited to the fingerprint image. For example, theminutia features generation apparatus 10 may calculate the minutiafeatures from an image related to a palm-print or the like.

Further, a plurality of steps (processes) are sequentially described ina plurality of flowcharts used in the above-mentioned description,however, the execution order of the steps to be executed in eachexemplary embodiment is not limited to the order in that description. Ineach exemplary embodiment, the order of the steps that are illustratedcan be subjected to a change in an extent which does not cause a troubleto the contents, such as execution of the respective steps in parallel.Alternatively, the contents described in the above-mentioned respectiveexemplary embodiments can be combined in such an extent that causesinconsistency in the contents.

As described above, the minutia features generation apparatus 10according to the first exemplary embodiment calculates the relationminutia features related to the relationship between two minutiae, inaddition to the minutia features such as the position and the directionof the minutia. The “contour skeleton count” is included in the relationminutia features. The contour skeleton count concerned has a differentnature from the inter-minutia ridge line count disclosed in Non PatentLiterature 2 and the like.

In the following, details of “inter-minutia ridge line count” disclosedin Non Patent Literature 2 and 3 will be described.

Note that, the “inter-minutia ridge line count” is usually calculatedfor all minutiae. Concretely, one minutia is selected from among all theminutiae appearing in the fingerprint image, and the selected minutiaand minutiae located in the vicinity thereof are connected by a straightline, and the number of the ridge line that intersects the straight lineis calculated as “inter-minutia ridge line count”.

“Inter-minutia ridge line count” is usually not affected by the imagedistortion as much as the position of the paired minutiae, however, theeffect of the image distortion becomes significant when the curvature ofridge line between the minutiae is large. For example, consider a caseof calculating the “inter-minutia ridge line count” from two fingerprintimages shown in FIG. 36. Here, it is assumed that the two fingerprintimages shown in FIG. 36 are the fingerprint images obtained from thesame finger of the same person (for example, fingerprint images relatedto an impressed fingerprint and a latent fingerprint). The minutia 391in FIG. 36(a) and the minutia 392 in FIG. 36(b), the minutia 393 in FIG.36(a) and the minutia 394 in FIG. 36(b) are the paired minutiae,respectively.

Referring to FIG. 36, it can be seen that there is a large difference incurving pattern (curvature) of the ridge lines between the minutia ineach figure. More concretely, in FIG. 36(a), the ridge lines includingthe ridge lines connecting the two minutiae is substantially straighttoward the upper right obliquely, whereas in FIG. 36(b), the ridge lineis greatly curved. In FIG. 36(a), when the “inter-minutia ridge linecount” regarding the minutiae 391 and 393 is calculated, the“inter-minutia ridge line count” becomes “0” since there is no ridgeline that intersects the straight line connecting the two minutiae. Incontrast, when calculating the “inter-minutia ridge line count” in FIG.36 (b), “inter-minutia ridge line count” becomes “6”, since the ridgeline that interests the straight line connecting the two minutiae is 6lines (6 points, circle in the figure represents the intersection).

In this way, despite the fingerprint images obtained from the sameperson, the “inter-minutia ridge line count” may differ greatly.Further, in order to eliminate the above inconvenience, even if acountermeasure is taken such that the same ridge line is not countedtwice, the number of ridge lines intersecting with the straight line inFIG. 36(b) becomes 3, in which “inter-minutia ridge line count” is stillvery different.

The “inter-minutia ridge line count” disclosed in Non Patent Literature2 and 3 is vulnerable to noise such as image distortion, so that highmatching accuracy cannot be obtained by the matching process using“inter-minutia ridge line count”. Thus, “inter-minutia ridge line count”has poor robustness (robust nature) when image distortion issignificant, and there is a limit to the contribution to improvingmatching accuracy (may also cause a deterioration of matching accuracy).As described above, in case a latent fingerprint with significant imagedistortion is to be matched, when using the “inter-minutia ridge linecount” as disclosed in Non Patent Literatures 2 and 3, a difference maybe brought about in “inter-minutia ridge line count” between the latentfingerprint and the impressed fingerprint as the counter-fingerprint.Therefore, high-precision matching cannot be expected with such matchingprocessing using the “inter-minutia ridge line count”. In addition,since it is not possible to extract a sufficient number of minutiae froma small area of the latent fingerprint, in case the number of minutiaeis small, there is little information to calculate the “inter-minutiaridge line count”, thus high-precision matching cannot be expected.

On the other hand, the minutia features generation apparatus 10according to the first exemplary embodiment treats the skeleton as thecontour line and calculates the number of contour lines (the skeleton,the ridge line) existing between two minutiae as the inter-minutia ridgeline count between tow minutiae. Further, as described above, theinter-minutia ridge line count calculated by treating the skeletons ascontour lines (number of contour core lines) is characterized by that itis not easily affected by image distortion (it is robust against imagedistortion). As a result, by using the minutia (feature) values for thematching processing of the two fingerprint images, it is possible toaccurately perform determination of the paired minutiae and calculationof the matching score on the file side.

Further, Patent Literature 1 discloses “ridge line connection relatedinformation” between the two neighboring minutiae as a new minutiafeature (feature amount). In more detail, in Patent Literature 1,information related to other minutiae existing on a ridge existing inthe vicinity of the minutia is referred to as “ridge line connectionrelated information” (see paragraph of Patent Literature 1).

Here note that, in the fingerprint image, the skeleton may beinterrupted due to the influence of noise etc. In FIG. 37, consider acase in case there is a discontinuity (blank) between the minutia P58and the route measurement starting point Q54′″. In this case, in themethod disclosed in Patent Literature 1, the first minutia 403 obtainedby tracing the skeleton from the route measurement starting point Q54′″is extracted as the nearest neighbor minutia, however, the minutia P58located at a faster point beyond the first minutia 403 is not extractedas the nearest neighbor minutia. However, the target minutia P54 and theminutia P58 are located close enough, and thus without extracting theminutia P58 as the nearest neighbor minutia and without calculating theminutia features, it is not expectable to contribute to improve thematching accuracy. That is, since minutia features such as “ridge lineconnection related information” disclosed in Patent Literature 1 cannotexhaustively extract the minutia located in vicinity of the minutia,contribution to the improvement of the matching accuracy is limited.

In contrast, the minutia features generation apparatus 10 according tothe first exemplary embodiment has no particular limitation onextraction of the minutiae located in the vicinity of the parentminutia, and exhaustively extracts the minutiae located in the vicinityof the parent minutia, so that the improvement of the matching accuracyusing the relation minutia features is expected.

Further, the minutia features generation apparatus 10 employs the ridgeline direction tracing instead of skeleton tracing when calculating thecontour skeleton count. Since the ridge line direction tracing is robustto the minutia(e) caused by noise existing on the tracing route, thecontour skeleton count can be calculated with high accuracy.

Furthermore, the minutia features generation apparatus 10 calculates therelational minutia features including the direction in which the childminutia exists as seen from the parent minutia (the child minutiarelative direction) and the position of the child minutia as seen fromthe nearest neighbor point (the child minutia relative position). Sincethese minutia features are also robust against image distortion, theycan be effectively used as minutia features when matching the twofingerprint images.

Furthermore, in case the two minutiae are directly connected byskeleton(s), the minutia features generation apparatus 10 specifies(identifies) the connection form of the two minutiae. By using suchconnection type as the minutia features used for the matching process,more accurate matching process is possible. For example, in case nominutia features are introduced depending on the connection type, thetwo minutiae as shown in FIG. 24(a) and FIG. 24(b), and the two minutiaeas shown in FIG. 25(d) may be difficult to, be distinguished by thecontour skeleton count. However, by using the connection type as theminutia features, it is possible to more accurately perform the matchingbetween the minutiae as shown in FIG. 24 (a) and FIG. 25 (d).Alternatively, since TYPE16 as shown in FIG. 26(c) and TYPE14 as shownin FIG. 26 (a) have a special skeleton form, which are not normallyconsidered to appear outside the core (fingerprint center), accuratematching results can be obtained by using the connection type relatedminutia features for matching processing. In particular, the connectiontype calculated by the minutia features generation apparatus 10 iseffective in the minutiae pairing process. Concretely, in case certainminutiae are paired at a particular timing in the pairing determinationprocess, child minutiae each other having the same type of connectiontype can also be determined as paired minutiae. For example, in FIG. 17,if the parent minutia 371 and the parent minutia 381 are determined tobe paired at a specific timing, the child minutia 374 and the childminutia 383 connected to each parent minutia by the connection typeTYPE12 can be immediately determined as the paired minutiae.

Part or all of the above-mentioned exemplary embodiments can bedescribed as the followings but is not limited to the followings.

<Mode 1>

A minutia features generation apparatus according to the first aspect,as described above.

<Mode 2>

The minutia features generation apparatus, preferably according to themode 1, wherein the calculation part firstly selects the first minutiafrom the plurality of minutiae, followed by extracting a predeterminednumber of minutiae, arranged in the order of proximity to the firstminutia; and sets a predetermined number of the extracted minutiae asthe second minutiae.

<Mode 3>

The minutia features generation apparatus, preferably according to mode1 or 2, wherein the generation part generates a ridge line directiondata including information related to a ridge line direction at eachpoint on the ridge line(s) making up the image; and the calculation partcalculates a trace line by tracing a ridge line direction using theridge line direction data, and identifies the nearest neighbor point.

<Mode 4>

The minutia features generation apparatus, preferably according to mode3, wherein the calculation part calculates a minutia direction thatfeatures at least a direction of the first minutia; identifies a firstnearest neighbor point candidate, which is a candidate of the nearestneighbor point by performing the ridge line direction tracing in adirection directed to a first trace direction which is the samedirection as the minutia direction; identifies a second nearest neighborpoint candidate, which is a candidate of the nearest neighbor point byperforming the ridge line tracing in a direction directed to a secondtrace direction which is an opposite direction of the minutia direction;and identifies the nearest neighbor point from the first and secondnearest neighbor point candidates, based on a closer distance to thesecond minutia.

<Mode 5>

The minutia features generation apparatus, preferably according to mode4, wherein the calculation part calculates one of the relational minutiafeatures, as defined by a result whether the trace direction used foridentification of the nearest neighbor point is the same direction asthe minutia direction of the first minutia or an opposite directionthereof.

<Mode 6>

The minutia features generation apparatus, preferably according to mode5, wherein the calculation part calculates, as one of the relationminutia features, a position where the second minutia locates based onthe trace direction used for identification of the nearest neighborpoint.

<Mode 7>

The minutia features generation apparatus, preferably according to anyone of modes 1 to 6, wherein the calculation part calculates, withrespect to the minutia(e) extracted from the skeletons, a position, adirection, and a type as minutia features that features the minutia.

<Mode 8>

The minutia features generation apparatus, preferably according to mode7, wherein the calculation part calculates a connection type, as aminutia feature, the connection type being between the first minutia andthe second minutia, in case the first minutia and the second minutia isconnected by at least one or more skeletons.

<Mode 9>

The minutia features generation apparatus, preferably according to mode8, wherein the calculation part calculates the connection type on thebasis of: a direction and a type related to each of the first and secondminutia; a number of connecting skeleton(s) between the first and thesecond minutia; and a type of a skeleton connected to the other minutiaamong three skeletons which form a bifurcation point, in case at leastone of the first and the second minutia is a bifurcation type.

<Mode 10>

The minutia features generation apparatus, preferably according to mode9, wherein the calculation part calculates a type of each of the threeskeletons in accordance to quadrant in which any of three skeletonsforming the bifurcation point exists, on a two-dimensional coordinatesystem which comprises an origin at the location of the bifurcationpoint, and a Y-positive direction as a direction directed to the minutiadirection featuring the bifurcation point.

<Mode 11>

The minutia features generation apparatus, preferably according to anyone of modes 1 to 10, comprising: an output part configured to output toan external apparatus at least information related to the relationminutia features calculated by the calculation part.

<Mode 12>

A system according to the second aspect, as described above.

<Mode 13>

A minutia features generation method according to the third aspect, asdescribed above.

<Mode 14>

A program according to the fourth aspect, as described above.

Note that modes 12 to 14 are possible to be extended like the way incase of mode 1, which is extended to modes 2 to 11.

Each disclosure of the above-mentioned Patent Literatures and so on thathave been cited is incorporated herein in its entirety by reference.

Modification and adjustment of each exemplary embodiment or each exampleare possible within the scope of the overall disclosure (including theclaims) of the present invention and based on the basic technicalconcept of the present invention. Various combinations or selections(including partial deletion) of various disclosed elements (includingeach element in each claim, each element in each exemplary embodiment oreach example, and each element in each drawing) are possible within thescope of the overall disclosure of the present invention. That is, thepresent invention naturally includes various variations andmodifications that could be made by those skilled in the art accordingto the overall disclosure including the claims and the technicalconcept. With respect to numerical value range(s) described herein inparticular, arbitrary numerical value(s) and a small range(s) includedin the numerical value range should be construed to be specificallydescribed even unless otherwise explicitly described.

REFERENCE SIGNS LIST

-   10, 100: minutia features generation apparatus-   11: CPU (Central Processing Unit)-   12: memory-   13: input and output interface-   14: NIC (Network Interface Card)-   20: database-   30: matching apparatus-   101: input part-   102: generation part-   103: extraction part-   104: calculation part-   201,221: fingerprint image input part-   202, 222: skeleton image generation part-   203, 223: minutia extraction part-   204, 224: minutia features calculation part-   205: minutia features output part-   211: basic minutia features calculation part-   212: relation minutia features calculation part-   213: connection type calculation part-   225: database (DB) access part-   226: matching part-   227: matching result output part-   301-317, 321-326, 391-394, 402, 403: minutia-   301 bifurcation point-   332: minutia direction-   333-335, 342: trace end point-   341,351, 421: end point-   352: dotted line-   353: ridge line direction data-   361,373: nearest neighbor point-   362, 372, 374, 375, 382, 383: child minutia-   371,381: parent minutia-   401,422: skeleton

1. A minutia features generation apparatus, comprising: an input partconfigured to input an image, formed as a curved stripe pattern by aridge line(s); a generation part configured to generate a skeletonimage, formed by extracting a skeleton(s) from the image; an extractionpart configured to extract a plurality of minutiae from the skeletonimage; and a calculation part configured to calculate a relation minutiafeature(s) representing relationship between a first minutia and asecond minutia among the plurality of minutiae, wherein, the calculationpart calculates as one of the relation minutia features defined by acrossing count of the skeleton(s) and a straight line connecting fromthe second minutia to a nearest neighbor point which is a point on atrace line traced by tracing starting from the first minutia, whichpoint is located at a shortest line distance from the second minutia. 2.The minutia features generation apparatus according to claim 1, whereinthe calculation part firstly selects the first minutia from among theplurality of minutiae, followed by extracting a predetermined number ofminutiae arranged in the order of proximity to the first minutia; andsets a predetermined number of the extracted minutiae as the secondminutia(e).
 3. The minutia features generation apparatus according toclaim 1, wherein the generation part generates a ridge line directiondata including information related to a ridge line direction at eachpoint of the ridge line(s) making up the image; and the calculation partcalculates a trace line by tracing the ridge line direction using theridge line direction data, and identifies the nearest neighbor point. 4.The minutia features generation apparatus according to claim 3, whereinthe calculation part calculates a minutia direction that features atleast a direction of the first minutia; identifies a first nearestneighbor point candidate, which is a candidate of the nearest neighborpoint by performing the ridge line direction tracing in a directiondirected to a first trace direction which is the same direction as theminutia direction; identifies a second nearest neighbor point candidate,which is a candidate of the nearest neighbor point by performing theridge line direction tracing in a direction directed to a second tracedirection which is an opposite direction of the minutia direction; andidentifies the nearest neighbor point from the first and second nearestneighbor point candidates, based on a closer distance to the secondminutia.
 5. The minutia features generation apparatus according to claim4, wherein the calculation part calculates one of the relational minutiafeatures, as defined by a result whether the trace direction used foridentification of the nearest neighbor point is the same direction asthe minutia direction of the first minutia or an opposite directionthereof.
 6. The minutia features generation apparatus according to claim5, wherein the calculation part calculates, as one of the relationminutia features, a position where the second minutia locates based onthe trace direction used for identification of the nearest neighborpoint.
 7. The minutia features generation apparatus according to claim1, wherein the calculation part calculates, with respect to the minutiaeextracted from the skeletons, a position, a direction and a type, asminutia features that feature the minutia.
 8. The minutia featuresgeneration apparatus according to claim 7, wherein the calculation partcalculates a connection type, as a minutia feature, the connection typebeing between the first minutia and the second minutia, in case thefirst minutia and the second minutia are connected by at least one ormore skeletons.
 9. The minutia features generation apparatus accordingto claim 8, wherein the calculation part calculates the connection typeon the basis of: a direction and a type related to each of the first andsecond minutia; a number of connecting skeleton(s) between the first andthe second minutia; and a type of a skeleton connected to the otherminutia among three skeletons which form a bifurcation point, in case atleast one of the first and the second minutia is a bifurcation type. 10.The minutia features generation apparatus according to claim 9, whereinthe calculation part calculates a type of each of the three skeletons inaccordance to quadrant in which any of three skeletons forming thebifurcation point exists, on a two-dimensional coordinate system whichcomprises an origin at the location of the bifurcation point, and aY-positive direction as a direction directed to the minutia directionfeaturing the bifurcation point.
 11. The minutia features generationapparatus according to claim 1, further comprising: an output partconfigured to output to an external apparatus at least informationrelated to the relation minutia features calculated by the calculationpart.
 12. A system, comprising: the minutia features generationapparatus according to claim 1, and a matching apparatus configured toperform matching of images using the minutia features generated by theminutia features generation apparatus.
 13. A minutia features generationmethod, comprising: inputting an image formed as a curved stripe patternby a ridge line(s); generating a skeleton image formed by extracting askeleton(s) from the image; extracting a plurality of minutiae from theskeleton image; and calculating a relation minutia feature(s)representing relationship between a first minutia and a second minutiaamong the plurality of minutiae, wherein the calculating is performed bycalculating as one of the relation minutia features defined by acrossing count of the skeleton(s) and a straight line connecting fromthe second minutia to a nearest neighbor point which is a point on atrace line traced by tracing starting from the first minutia, whichpoint is located at a shortest line distance from the second minutia.14. A computer readable non-transient recording medium storing a programcausing a computer to perform processing of: inputting an image formedas a curved stripe pattern by a ridge line(s); generating a skeletonimage formed by extracting a skeleton(s) from the image; extracting aplurality of minutiae from the skeleton image; and calculating arelation minutia feature(s) representing relationship between a firstminutia and a second minutia among the plurality of minutiae, wherein,the calculating is performed by calculating as one of the relationminutia features defined by a crossing count of the skeleton(s) and astraight line connecting from the second minutia to a nearest neighborpoint which is a point on a trace line traced by tracing starting fromthe first minutia, which point is located at a shortest line distancefrom the second minutia.
 15. The minutia features generating methodaccording to claim 13, wherein the calculating is performed by firstlyselecting the first minutia from among the plurality of minutiae,followed by extracting a predetermined number of minutiae arranged inthe order of proximity to the first minutia; and sets a predeterminednumber of the extracted minutiae as the second minutia(e).
 16. Theminutia features generating method according to claim 13, wherein thegenerating is performed by generating a ridge line direction dataincluding information related to a ridge line direction at each point ofthe ridge line(s) making up the image; and calculating a trace line bytracing the ridge line direction using the ridge line direction data,and identifies the nearest neighbor point.
 17. The minutia featuresgenerating method according to claim 16, wherein the calculating isperformed by calculating a minutia direction that features at least adirection of the first minutia; identifying a first nearest neighborpoint candidate, which is a candidate of the nearest neighbor point byperforming the ridge line direction tracing in a direction directed to afirst trace direction which is the same direction as the minutiadirection; identifying a second nearest neighbor point candidate, whichis a candidate of the nearest neighbor point by performing the ridgeline direction tracing in a direction directed to a second tracedirection which is an opposite direction of the minutia direction; andidentifying the nearest neighbor point from the first and second nearestneighbor point candidates, based on a closer distance to the secondminutia.
 18. The minutia features generation method according to claim17, wherein the calculating is performed by calculating one of therelational minutia features, as defined by a result whether the tracedirection used for identification of the nearest neighbor point is thesame direction as the minutia direction of the first minutia or anopposite direction thereof.
 19. The minutia features generation methodaccording to claim 18, wherein the calculating is performed bycalculating, as one of the relation minutia features, a position wherethe second minutia locates based on the trace direction used foridentification of the nearest neighbor point.
 20. The minutia featuresgeneration method according to claim 13, wherein the calculating isperformed by calculating, with respect to the minutiae extracted fromthe skeletons, a position, a direction and a type, as minutia featuresthat feature the minutia.